reproducibility and validity of a chinese food frequency ... · reproducibility and validity of a...

www.besjournal.comBIOMEDICAL AND ENVIRONMENTAL SCIENCES 23 (suppl.) , 1-38 (2010)

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Reproducibility and Validity of a Chinese Food Frequency Questionnaire

Wen-Hua ZHAO*,1, Zhi-Ping HUANG#, Xin ZHANG∆, Li HE∆, Water WILLETT§, Jun-Ling WANG*, Kyoko HASEGAWA+,

and Jun-Shi CHEN∆

*Chinese Center for Disease Control and Prevention, Beijing 100050, China; #Department of Community and Preventive Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA;

∆Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention, Beijing 100050, China; §Department of Nutrition,

Harvard School of Public Health, Boston, MA 02115, USA;+Kagawa Nutrition University, Saitama 350-02, Japan

ABSTRACTObjectives

This study was design to develop a semi-quantitative Chinese Food Frequency Questionnaire (FFQ) and to conduct a validation study for the questionnaire.Methods

Based on the survey experience in recent years, a new Chinese food frequency questionnaire (FFQ) with 149 items in 17 food categories was developed. A validation study on this new FFQ was conducted in Jiangsu and Beijing of China between 1999 and 2001. The period of study covered 1 year and the FFQ was validated by comparing with data obtained by a six repeated 24-hour recalls for 3 consecutive days, or a totally 18-day 24-hour recall throughout the year. A total of 271 healthy adult subjects were enrolled in the study.

Food and nutrient intakes measured by the 18-day dietary recalls and food frequency questionnaires (FFQs) were computed in the National Institute for Nutrition and Food Safety, China CDC using the existing nutrition database. The average daily intake of foods and nutrients over the 18-day recall was used to compare with FFQ1 and FFQ2, which was conducted at the beginning and the end of the year, respectively. All statistical analyses were carried out using SAS software version 6.12.Results

The reproducibility of FFQ in this study was evaluated at three levels between FFQ1 and FFQ2, i.e. comparison of the mean intake of foods and nutrients; correlation analysis of their intake; and cross-classification and agreement on their corresponding intake. The results showed a high degree of reproducibility for both foods and nutrients. Except for wheat flour and fishes, there were no significant differences in the mean intake of all other foods including rice, other cereals, fresh vegetables, salted vegetables, fresh fruits, nuts, pork, poultry, egg, milk, vegetable oil, soy sauce, salt, and liquor; and this is also true for all nutrients except thiamin. The correlation coefficients ranged from 0.43 to 0.90 for foods and 0.23 to 0.73 for nutrients.

Relative validity was tested by comparing the results of food consumption and nutrient intake from both FFQ1 and FFQ2 with those from the average of the 18-day 24-hour recall. The relative validity of FFQ1 was performed in the absence of the possible bias due to a learning effect in FFQ2. This was closer to the real situation where subjects were deprived of any previous experience in quantifying their diet. However, the relative validation of FFQ2 covered the same period as the 24-hour recall.

By comparing the mean intake of foods and nutrients between FFQ1 and FFQ2 and the 24-hour recall significant differences were revealed in most foods and nutrients.

The crude correlation coefficients between FFQ1 and means of the 24-hour recall ranged from 0.12 to 0.87 for foods and from 0.33 to 0.63 for nutrients. The crude correlation coefficients between FFQ2 and the 24-hour recall ranged from 0.33 to 0.85 for foods and from 0.22 to 0.84 for nutrients. The strongest correlations were

0895-3988/2010CN 11-2816/QCopyright © 2010 by China CDC

1Correspondence should be addressed: Wen-Hua ZHAO, professor, National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Precention, Beijing 100050, China. Biographical note of the first author: Wen-Hua ZHAO, professor, majoring in nutrition epidemiology and chronic disease control and prevention.

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found for staple food (rice and wheat flour), pork, poultry and fishes, milk, and liquor. The weakest correlations were found for foods which are not consumed regularly such as potatoes, nuts, legume, and products; and also for fresh vegetables. Adjustment for energy and for attenuation improved correlation for nutrients. The correlation coefficients ranged from 0.27 to 0.86 for FFQ1 and the 24-hour recall and ranged from 0.39 to 0.99 for FFQ2 and the 24-hour recall. Discussion Reproducibility: In conducting a reproducibility evaluation, it is unrealistic to administer the questionnaire at a very short interval, such as in a few days or weeks. When a longer interval of time is used, true changes in dietary intake, as well as variation in response, may lead to reduced reproducibility. This study used one year as an interval between the two interviews which was the most desirable one used in other studies. The reproducibility of FFQ in this study was evaluated in three aspects between FFQ1 and FFQ2, i.e. comparison of the mean intake of foods and nutrients; correlation analysis of their intake; and cross-classification and agreement on their intake A high degree of reproducibility was shown for both food consumption and nutrient intake. Validity: Relative validity was tested by comparing the results of food consumption and nutrient intake from both FFQ1 and FFQ2 with those from the average of the 18-day 24-hour recall. The correlation of FFQ2 with the average of the 18 day 24-hour recalls was generally stronger than that of FFQ1. The relative validity of FFQ1 was performed in the absence of the possible bias due to a learning effect in FFQ2. This was closer to the real situation where subjects were deprived of any previous experience in quantifying their diet. However, the relative validation of FFQ2 covered the same period as the 24-hour recall.

Among the available and feasible comparison methods of validating a FFQ, diet records are likely to have the least correlation with FFQ which are due to the restrictions imposed by a fixed list of foods, memory, perception of portion size, and interpretation of questions. These sources of error are minimally shared by diet records because diet records are open-ended, do not depend on memory (foods are recorded on a meal-by-meal basis), and allow direct assessment of portion size. The primary alternative for the use of diet records as a standard of evaluating FFQ is the collection of multiple 24-hour recalls. The results of an evaluation of relative validation depend on several factors which include choice of reference method, the degree of homogeneity of intake values within the population, recall period, and the number of the days recorded. The standard method in our study was a six repeated 24-hour recall for three consecutive days, or a totally 18-day 24-hour recall, over one-year period. Our study subjects were a group of adult residents with a fairly fixed lifestyle. These may partly contribute to stronger correlations obtained in our study.

A trend that FFQ overestimates the mean intake for most of the food groups and nutrients included in the study has been observed. There have been few studies to reveal information on over- or under-estimates of both food consumption and nutrient intake by FFQ and the 24-hour recall. The overestimates of both food and nutrient intake in our study may possibly be explained by the fact that the 24-hour recall estimates of food and nutrient intake are derived directly from reports of actual diet of 18 days and that in FFQ the intake comes from summaries or averages of foods consumed during the year and the 18 day 24-hour recall may be not long enough to estimate individual one year diet intake since diet variation exists cross the season and day to day. The results of high degree of reproducibility strongly support the assumption that FFQ could reflect the one year dietary information of the individual.

Despite some overestimation of both foods and nutrients by FFQs, agreement on cross-classification is comparable to what other studies have shown, and classification in the same quartile in our study shows a mean of over 45% agreement, while classification in the same and next quartile reveals an mean agreement over 75 %. Application: FFQ developed by this study has been applied in several other studies including the Chinese National Nutrition and Health Survey in 2002.Conclusion

In this study, the reproducibility and validity of FFQ were all satisfactory. The results have shown that FFQ can be used to classify study subjects according to their food consumption or nutrient intake over a one-year period. These findings have also confirmed that FFQ is an appropriate instrument to measure the usual food consumption and nutrient intake, as well as to assess the dietary patterns of adult Chinese. It could be used in studies with different purposes, especially in studying the relationship between diet, nutrition and chronic diseases.

3REPRODUCIBILITY AND VALIDITY OF A CHINESE FOOD FREQUENCY QUESTIONNAIRE

INTRODUCTION

Background and Significances

It has been agreed for many years that the nature and quality of diets exert their effect on the risk of chronic diseases. The transition of lifestyle, especially the transition of dietary pattern in the Chinese people, along with the development of national economy has caused significant changes of disease pattern in China since the 1950s, particularly in the last two decades. Chronic diseases now account for more than 70% of the total mortality in China[1]. Intervention trials have shown that dietary modifications could lower the risk of cancer and heart disease[2-3]. Therefore, the global incidences of cancer, coronary heart disease, and other chronic diseases could be substantially reduced by dietary means[4]. It is particularly important for studies on relationships between diet and chronic diseases that the method for dietary assessment used for collecting the long term food consumption information is valid. One of bottlenecks of studies focusing on dietary factors is due to the limitations of the methodology used to estimate dietary intakes. The choice of one method or another depends not only on the type of information sought, but also on the practicability of the method used. A number of epidemiological studies have been conducted throughout China over the past years. Among the various method used, the 24-hour recall method has been widely used in most studies[5], which is based on foods and their amounts actually consumed by an individual on one or more specific days. It is based on an in-depth interview conducted by a trained interviewer. For practical reasons, collection of multiple days of intakes is not feasible for most epidemiological studies involving large numbers of individuals[6]. The food diary approach is able to obtain long-term food consumption information, but it is not feasible in large studies, especially in less educated populations.

Because short-term recall and dietary record methods are generally expensive, unrepresentative of usual intakes if only a few days are assessed, and inappropriate for assessment of past diet, investigators have sought alternative methods for measuring long-term dietary intakes.

During the 1950s, Stephanik and Trulson (1962), Wiehl and Reed (1960), and Marr (1971) developed food frequency questionnaires (FFQs) and evaluated their role in dietary assessment. The interest in food frequency questionnaires has greatly increased more recently. Multiple investigators have converged, apparently independently, toward the use of food

frequency questionnaires as the method of dietary assessment best suited for most epidemiologic applications. In the 1980s and the 1990s, substantial refinement, modification, and evaluation of food-frequency questionnaires occurred[6].

The underlying principle of the food frequency approach lies in the hypothesis that average long-term diet, for example, intake over weeks, months, or years, rather than intake on a few specific days, is a conceptually important exposure. Therefore, it may be advantageous to sacrifice precise intake measurements obtainable on one or a few days in exchange for more crude information relating to an extended period of time. Moreover, it is typically easier to describe one’s usual frequency of consuming a food than to describe what foods were eaten at any specific meal in the past[6].

Food frequency questionnaire (FFQ) designed to conduct qualitative or semi-quantitative dietary assessment, obtains retrospective information on the pattern of foods used during a longer, less precisely defined time period (e.g. daily, weekly, monthly or year) and is used to assess habitual intake of foods or specific classes of foods[7]. In the past decades, the use of semi-quantitative food frequency questionnaire in epidemiological studies of diet and disease relationships has been increased remarkably, because usual dietary intake over an extended period of time is more pertinent in assessing the relationship of nutrition with chronic disease than on a recent specific day or week[8].

The reproducibility and validity of FFQ have been examined in a wide variety of populations. In these studies, correlation coefficients have generally ranged from 0.5 to 0.7 for nutrient intakes measured by repeated FFQ at intervals of 1 to 10 years[9-12]. The validity of FFQ has been examined using multiple dietary records or dietary recalls as a gold standard and the average correlation coefficients for energy-adjusted nutrients have generally ranged from 0.6 to 0.7[10,13-15]. Thus, the FFQ approach appears to be robust across a wide range of populations. In addition, FFQ was relatively inexpensive, easy to complete and has become the primary dietary assessment method in epidemiological studies[16]. However, FFQ developed for studies in Asian population are scarce. A self-administered dietary history questionnaire has been developed and validated by Sasaki for the use in health education in Japan[17]. In China, comprehensive FFQ has not been widely used. Only a few qualitative FFQs have been used in some nutritional studies[18]. A simplified FFQ with 17 questions on food consumption was developed and

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used in a large rural study in 1996-1997. FFQ with 84 questions on consumption of 16 food categories was used in a study comparing dietary consumption data with various health indicators of elderly people in 4 geographical areas of China in 1998[19]. FFQ used in the Shanghai breast cancer study included more food items, but they were specific for the Shanghai area and unsuitable for other regions of China[20].

The conducting of nutritional studies in China can provide a great opportunity to examine the relations between diet and chronic diseases. This will contribute to overall scientific knowledge and should lead to effective prevention of chronic diseases, which is beneficial not only to China but also to the other parts of the world. However, a major obstacle is the absence of validated method presently to assess usual long-term dietary intake in China. The commonly used methods nowadays to collect dietary information in this country are three-day dietary recall and three-day household food consumption recording which actually weighs and records all the foods brought into the household and the remaining foods at the end of the three days[21]. The fundamental limitation of these methods is that the dietary information in any given three-day period may not be typical or representative of usual intake because diet is highly variable from day to day. Besides, these methods only measure current intake and can not fully represent long-term past dietary intake which is believed to be related to non-communicable diseases (NCDs) more than current short-term intake. In addition, the household recording method does not provide individual intake data for each family member, which is necessary in NCD studies and may become more problematic because “eating-out” is increasing in China. Thus, dietary recalls and household food recording are not suitable for most nutritional epidemiological studies that require an assessment of usual long-term individual dietary intake.

It is urgently needed to develop and validate a Chinese food frequency questionnaire. It will serve as a valid dietary assessment tool and lead to effective prevention of NCDs in China. It will also greatly facilitate international comparison between studies on diet and NCDs.

A validation study for a Chinese food frequency questionnaire was conducted in China from October 1999 to December of 2000. It was a collaborative study between the Institute of Nutrition and Food Hygiene, Chinese Academy of Preventive Medicine (now known as the National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention), and Mount Sinai School of Medicine and Harvard School of Public Health which was

funded by the National Institute for Cancer Research, NIH, USA.

Study Hypotheses

China is now under a rapid transition both in diet and disease patterns. There is an urgent need to develop a dietary assessment tool to examine the relationships between diet and chronic diseases.

FFQ is a useful method in the collection of individual food consumption information in China.

The food items in the new FFQ could cover most of the foods in most regions of this country. In future studies in other regions and on special populations, FFQ can be modified accordingly, such as, adding special food items or deleting some other food items. Once a FFQ is established, it should be easier to be modified as needed.

Objectives

This study was designed to develop a semi-quantitative Chinese Food Frequency Questionnaire (FFQ) and to conduct a validation study for the questionnaire. The validation study was conducted among about 300 Chinese male and female healthy adults living in one urban and two rural areas of China. Specifically, the study aimed at:

1. To newly develop a comprehensive semi-quantitative Chinese FFQ on the bases of previous experience in China, USA, and Japan.

2. To evaluate the reproducibility of this FFQ and its validity by comparing with a repeated 24-hour dietary recall.

3. In a long-term perspective to evaluate prospectively the associations of food consumption and nutrient intakes with multiple chronic diseases outcomes (for examples, coronary heart disease, hypertension, diabetes, cancers, etc.).

STUDY DESIGN AND METHODOLOGY

Subjects

Enrolled in the validation study were 300 Chinese adults aged 25-64 years living in Taicang rural d is t r ic t and Wuxi c i ty of J iangsu province (representing southern China); and in Daxing rural district of Beijing municipality (representing northern China). The food consumption patterns of the residents involved are the typical southern or northern cuisine. About 100 adults (50 males and 50 females) were randomly selected from an official registry of local residents from each of the three regions. Pregnant or breast feeding women or subjects with illnesses were excluded due to possible


changes in their recent diet. Taicang and Daxing as two representative rural

areas A three-tier medical service network (county, township, and village) has been established in rural China. The health care system has been in place for 30 years to provide medical services for all the residents, and offers a unique opportunity to contact the subjects and conduct data collection. Besides, the villages have a very stable population base, which will facilitate the subject recruitment and tracking. They still follow the traditional Chinese diets and dietary habits with rice as the staple food in Taicang and wheat in Daxing. In general, their diets and dietary habits are quite representative of the rural areas in the southern and northern China, respectively. Thus, one rural village in Taicang and the other one in Daxing were randomly selected for this study.

Wuxi as a representative urban area Urban China is markedly different from rural Chinese society in terms of diet, lifestyle, and environmental exposures. There has been a rapid economic development in recent years in urban areas. More varieties of food items are available and urban residents consume more fish, animal products and more fat than rural residents. Wuxi is a medium sized city with a population of about 500 000 which is quite representative of southern urban China. In order to take account into rural-urban difference and dietary variation, Wuxi city was selected for this study.

FFQ’s Development and Pretest

FFQ with 149 food items in 17 categories (see Appendix 1) was constructed based on the experiences from China, USA and Japan. The frequencies of consumption were divided into: daily, weekly, monthly, yearly and never. The actual amount of food consumed was estimated in an individual manner. The quantitative variables were measured in terms of the Chinese liang (equivalent to 50 grams). FFQ was pre-tested in rural Beijing and urban Jiangsu and revised before the study.

Data Collection

Interviewer training A working manual including protocol, questionnaire, explanations for each questions, food code, etc. was developed as the standard tool for the team members of the project. A one-week systematic training was conducted before the formal data collection.

Data collection Experienced nutritionists in the Institute of Nutrition and Food Hygiene, Chinese Academy of Preventive Medicine worked in the field in collaboration with local health workers for

data collection. Randomly selected subjects were interviewed in their homes.

Food frequency data: Considering different levels of literacy, the newly developed FFQ was interviewer-administered. Food frequency information was collected twice using FFQ at the beginning and the end of the one-year study period, starting in October-November of 1999. FFQ contained approximately 149 food items, in which their usual intake frequency and usual portion size consumed over the past year were inquired. Relative portions of meat and vegetables in a mixed dish were asked in the meat and vegetable sections. A full cycle of seasons was thus provided so that the responses theoretically were independent of the time of year. Cooking methods were also inquired in the FFQ survey.

24-hour recall data (see Appendix 2): The subjects were asked to recall all of their food intakes including drinks and snacks during the day just before the three- consecutive-day interview, lasting about 30 minutes for each one. The interviewer used a standardized form and a standardized approach for probing questions. A food checklist was used to remind the subjects of easily forgotten foods. Three-dimensional food models were used to help the subjects estimate the amount of foods consumed.

Six repeated three-day 24-hour recalls were collected from each subject at two-month intervals over the one-year period to elucidate day-to-day variation and seasonal variation in foods and medium-term drifts in food habits over the study period. In order to take account of the food differences between weekdays and weekends, 24-hour recalls were conducted on both weekdays and weekends. Since there were six repeated three-day dietary recall interviews, each time we started to interview a subject on a different day of a week (Monday, Tuesday, Wednesday, Thursday, Saturday, and Sunday, respectively) and continue for the next two days. Thus, each subject had a total of 18 dietary recalls, i.e. three recalls on Monday, Tuesday, Wednesday and Sunday, two recalls on Thursday, Friday, and Saturday (Appendix 3).

The consumption information of several specific food items including salt, soy source, and cooking oil were collected on the basis of the whole family and then got the average for the subject.

Portion size and models: During the interview, three-dimensional food models consisting of different sizes of cups, bowls and dishes were used to help the subjects estimate the amount of foods consumed. The converting references standard was made by the professional staff in NINFS, China CDC (see Appendix 4). Virtually all the foods could be measured by these models. For foods with a natural

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portion size, such as apple, the natural portion size, that is, one or two apples, was used.

The data were sent to Beijing immediately after data collection finished at the sites so the answers from different time points of the survey could not be compared.

Data Management

All food variables were checked for completeness at the end of each interview by both the interviewer and team leader in each site. Primary data checking was conducted by the staff in NINFS, China CDC, and all data obtained were entered into computer and have been stored there till now.

Data Analysis

All statistical analysis were carried out using SAS software version 6.12 (SAS Inc., Cary, NC, USA) in NINFS, China CDC. The level of significance was set at P<0.05.

The principle of food grouping is given in Appendix 5. Foods consumption by group and selected nutrient intake measured by the 18-day dietary recalls and FFQ were computed using the Chinese Food Composition Table database[22]. Intake levels of total energy, carbohydrate, protein, fat, fiber, carotene, vitamin A, vitamin E total, thiamin, riboflavin, niacin, vitamin C, sodium, calcium, iron, zinc, selenium, and cholesterol were calculated. Fat was further divided into three subgroups, i.e., saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA). Nutrients from food supplements were not included in the analysis in this study.

Several approaches including means comparison, corre la t ion coeff ic ient computa t ion , c ross-classification analysis were used to evaluate the reproducibility and validity of the FFQ[6,17].

1. Reproducibility Firstly, means of food consumption and nutrient intake were compared between FFQ1 and FFQ2. The mean difference and percentage difference were calculated. Student t-test was used to test the significance of differences.

Secondly, reproducibility of FFQ was estimated by calculating the correlation coefficient between FFQ1 (data collected at beginning) and FFQ2 (data collected at the end of one-year period) for each food group and selected nutrients. Spearman correlation analysis was used for food consumption and Pearson correlation analysis was used for nutrient comparison.

Thirdly, in order to estimate the level of misclassification and overall agreement between FFQ1 and FFQ2, the figures of food and nutrient intake derived from FFQ1 and FFQ2 were divided

into quartiles to examine how well the individual intake data were classified. Classification error and overall agreement were examined.

2. Validity The average daily consumption of foods and intake of nutrients obtained from FFQ was compared with those obtained from the 18-day 24-hour recall to validate FFQ (the golden standard).

Firstly, means of food consumption and nutrient intake were compared between FFQ1 and the 24-hour recall, and between FFQ2 and the 24-hour recall, respectively. The mean difference and percentage difference between FFQ1 and the 24-hour recall, and FFQ2 and the 24-hour recall were calculated. Student t-test was used to compare the means.

Secondly, relative validity of FFQ was tested by comparing both FFQ1 and FFQ2 with the average of the 18-day 24-hour recall (data collected during the one-year period) respectively. The correlation coefficient between FFQ1 and the 24-hour recall, and between FFQ2 and the 24-hour recall were calculated for each food comparison and selected nutrients. The Spearman correlation coefficient was used for food groups and Pearson correlation coefficient was used for nutrient comparison.

Thirdly, in order to estimate the level of misclassification and overall agreement between FFQ1 and the 24-hour recall, and between FFQ2 and the 24-hour recall, the figures of foods and nutrients derived from FFQ1, FFQ2 and the 24-hour recall were divided into quartiles to examine how well the individual data were classified. Classification error and overall agreement were examined.

The relative validity of FFQ1 was performed in the absence of the possible bias due to a learning effect in FFQ2. This was closer to the real situation where subjects were deprived of any previous experience in quantifying their diet. However, the relative validation of FFQ2 covered the same period as the 24-hour recall.

Since the method for collecting the consumption information on salt, soy sauce, and cooking oil was the same in both FFQ and the 24-hour recall, the analysis of these three food groups were not included in the validity analysis between the 24-hour recall and FFQ2.

For nutrients a log transformation was used to obtain a sample distribution of intake values which was closer to normal. Nutrient intakes were adjusted for total energy intake and attenuation.

A correction for attenuation was carried out in order to adjust for within-subject random errors of the reference measurement[22-23]. It was calculated using the ratio of within- to between- person variance measured from the 18-day 24-hours recall. The formula for this corrected correlation was calculated


TABLE 1

General Information of the Subjects (x±s)

Note. *SBP: systolic blood pressure; **DBP: diastolic blood pressure.

Indicator Male (n=131) Female (n=140) Total (n=271)

Age (year) 44.5 ± 11.4 46.2 ± 10.1 45.4 ± 10.8

Height (cm) 169.5 ± 6.1 156.2 ± 5.3 162.8 ± 8.8

Weight (kg) 68.4 ± 10.5 57.8 ± 9.1 63.1 ± 11.1

BMI (kg/m2) 23.8 ± 3.4 23.7 ± 3.6 23.7 ± 3.5

SBP*(mm Hg) 125.7 ± 15.2 119.9 ± 17.0 122.8 ± 16.4

DBP** (mm Hg) 82.1 ± 8.7 77.4 ± 9.4 79.7 ± 9.3

TABLE 2

Comparison of Food Consumption between FFQ1 and FFQ2 (x±s, g/d)

Food Group FFQ1 FFQ2 Mean Difference# % of Difference#

Rice 265.5 ± 177.7 273.0 ± 171.6 7.5 2.8

Wheat Flour 136.0 ± 182.0 146.9 ± 193.0 10.9* 8.0

Other Cereals 22.2 ± 35.9 21.2 ± 32.1 -1.0 -4.5

Potatoes 19.4 ± 23.5 20.0 ± 18.9 0.6 3.1

Legume & Products 59.7 ± 69.4 56.4 ± 55.0 -3.3 -5.5

(to be continued on the next page)

as rc = ro [ 1+ (S2 w/ S2 b)/n]1/2 where ro is the observed correlation, S2w is the within-person variation, and S2b is the between-person variation and n is the number of replicate measurements. For this calculation n=18 represented each day of the 24-hour recall. However, such corrections are only permitted when the variables follow a normal distribution, as in the case for nutrients.

RESULTS

Information on food consumption greater than 10 g/d (apart from salt), and selected nutrient intake including energy, protein, fat, carbohydrate, fiber, carotene, vitamin A, thiamin, riboflavin, niacin, vitamin C, vitamin E total, sodium, calcium, iron, zinc, selenium, cholesterol, SAF, MUFA, and PUSA

is presented as below.

General Information

A total of 312 subjects were enrolled at the beginning of the study. Two subjects had severe illness during the study; 12 subjects had incomplete data for the 18-day 24-hour recall, and 3 and 5 subjects had incomplete data for FFQ1 or FFQ2 respectively, and 17 subjects were deleted according to the regulation of data cleaning, whose energy intake was greater than 4 500 kcal per day or less than 800 kcal per day. Finally, the data from 271 subjects were used in the analysis accounting for 87% of the enrolled subjects. Table 1 shows the general information of the subjects. Detailed characteristics of the subjects are given in Appendix 6.

Reproducibility

1. Comparison of food consumption between FFQ1 and FFQ2 The consumption of major food items from FFQ1 and FFQ2 is presented in Table 2. Except for wheat flour and fishes, there were no significant differences in the mean food consumption including rice, other cereals, potatoes, legume and products, fresh vegetables, salted vegetables, poultry, egg, milk,

vegetable oil, soy sauce, salt and liquor between FFQ1 and FFQ2; and the range of mean differences of food consumption was 0.6 to 17.5 gram per day. The percentage of difference was less than 5% in rice, other cereals, potatoes, fresh vegetables, nuts, pork, vegetable oil, salt, and liquor while it was greater than 10% in wheat flour, legume and products, salted vegetables, fresh fruits, poultry, fish, egg, milk, and soy sauce. See Table 2.

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TABLE 3

Comparison of Nutrients Intake between FFQ1 and FFQ2 (x±s)

Nutrients FFQ1 FFQ2 Mean Difference# % of Difference#

Energy (KJ/d) 10885.9 ± 2701.1 11020.6 ± 2591.7 134.7 1.2

Protein (g/d) 83.4 ± 24.03 83.7 ± 23.3 0.3 0.4

Fat (g/day) 78.5 ± 29.0 76.2 ± 26.4 -2.3 -3.0

Carbohydrate (g/d) 380.9 ± 106.1 396.0 ± 106.6 15.2* 4.0

Fiber (g/d) 12.8 ± 5.4 12.6 ± 5.3 -0.2 -1.8

Carotene (mg/d) 2645.5 ± 1435.3 2565.8 ± 1143.5 -79.7 -3.0

Vitamin A (mg/d) 277.3 ± 406.7 247.9 ± 239.4 -29.3 -10.6

Thiamin (mg/d) 1.34 ± 0.38 1.27 ± 0.34 -0.07** -5.22

Riboflavin (mg/d) 1.15 ± 0.40 1.15 ± 0.36 0.00 0.00

Niacin (mg/d) 17.4 ± 5.8 17.2 ± 5.6 -0.2 -1.20

Vitamin C (mg/d) 92.0 ± 38.1 91.7 ± 35.6 -0.2 -0.3

Vitamin E (mg/d) 31.7 ± 11.2 30.8 ± 10.4 -0.9 -2.8

Sodium (mg/d) 6285.2 ± 2392.6 6099.3 ± 1755.9 -186.0 -3.0

Note. #: Mean difference = (FFQ2-FFQ1); % of difference = Mean difference / FFQ1; * P<0.05.

Food Group FFQ1 FFQ2 Mean Difference# % of Difference#

Fresh Vegetable 293.5 ± 127.9 298.5 ± 117.5 5.0 1.7

Salted Vegetables 20.9 ± 20.0 19.2 ± 14.2 -1.7 -8.1

Fresh Fruits 208.0 ± 150.7 195.2 ± 134.9 -12.7 -6.1

Nuts 18.8 ± 27.7 18.6 ± 24.3 -0.2 -1.2

Pork 72.5 ± 56.4 71.8 ± 56.2 -0.7 -1.1

Poultry 11.6 ± 13.6 10.7 ± 13.3 -0.8 -7.2

Fishes 57.7 ± 58.7 52.7 ± 56.0 -5.0* -8.7

Egg 42.5 ± 31.1 39.4 ± 28.4 -3.1 -7.4

Milk 44.9 ± 131.5 48.7 ± 104.7 3.8 8.5

Vegetable Oil 34.7 ± 15.8 34.6 ± 14.0 -0.1 -0.3

Soy Sauce 13.4 ± 9.9 12.7 ± 8.2 -0.7 -5.3

Salt 9.2 ± 4.7 9.3 ± 3.5 0.1 1.2

Liquor 34.7 ± 15.8 33.3 ± 13.5 -1.4 -4.0

(continued)

2. Comparison of nutrient intake between FFQ1 and FFQ2 Except for carbohydrate, there were no significant differences in the intake of macro-nutrients including energy, protein and fat between FFQ1 and FFQ2. Except for thiamin, no significant differences were found in the intake of vitamins included carotene, vitamin A, riboflavin, niacin, vitamin C, and vitamin E. Significant differences were neither found in the intake of minerals included sodium, calcium,

iron, zinc, and selenium. It was also not significantly different for cholesterol and SFA, MUFA and PUFA. The percentage of the difference in energy, protein fat and carbohydrate ranged from 0.4% to 4.0%; that in vitamins ranged from 0.3% to 10.6%; the percentage difference in minerals including sodium, calcium, iron, zinc and selenium ranged from 0.02% to 3.0%; that in cholesterol was 6.1 % and in SFA, PUFA, and MUFA was 3.3%, 1.8%, and 4.3% respectively. See Table 3.

(to be continued on the next page)


(continued)

3. Correlation of food consumption between FFQ1 and FFQ2 The results of Spearman correlation analysis of food consumption between FFQ1 and FFQ2 is presented in Table 4. Correlation coefficient was 0.88, 0.90, and 0.82 for rice, wheat flour and other cereals, respectively. Correlation coefficient was 0.44 for fresh vegetables, 0.57 for fresh fruits, and 0.64 for legume and products. Correlation coefficient was 0.67 for pork, and 0.60 for poultry, 0.79 for fishes, respectively. Correlation coefficient for egg and milk was 0.49 and 0.76, respectively. Correlation coefficient for liquor was 0.57. For edible oil, soy sauce and salt, the consumption was estimated based on the family consumption data, and the correlation coefficient was 0.49, 0.57, and 0.69, respectively.

Note. #: Mean difference = (FFQ2-FFQ1); % of difference = Mean difference / FFQ1; * P<0.05; ** P<0.01.

Nutrients FFQ1 FFQ2 Mean Difference# % of Difference#

Calcium (mg/d) 596.1 ± 263.6 587.3 ± 240.6 -8.8 -1.5

Iron (mg/d) 24.2 ± 7.5 23.6 ± 7.1 -0.6 -2.4

Zinc (mg/d) 13.1 ± 3.6 12.7 ± 3.5 -0.4 -3.0

Selenium (mg/d) 49.3 ± 19.5 49.3 ± 18.6 0.0 0.0

Cholesterol (mg/d) 374.3 ± 220.4 351.4 ± 211.5 -22.8 -6.1

SFA (g/d) 20.1 ± 9.4 19.4 ± 9.0 -0.7 -3.34

MUFA (g/d) 33.0 ± 13.7 32.4 ± 13.4 -0.6 -1.8

PUFA (g/d) 21.9 ± 10.1 21.0 ± 8.5 -0.9 -4.3

TABLE 4

Spearman Correlations of Foods Consumption between FFQ1 and FFQ2 (n=271)

Food Group Correlation Coefficient*

Rice 0.88

Wheat Flour 0.90

Other Cereals 0.82

Potatoes 0.60

Legume & Products 0.64

Fresh Vegetable 0.44

Salted Vegetable 0.43

Fresh Fruits 0.57

Nuts 0.59

Pork 0.67

Poultry 0.60

Fishes 0.79

Egg 0.49

Milk 0.76

Vegetable Oil 0.57

Soy Sauce 0.69

Salt 0.49

Liquor 0.57

Note. *: P<0.001.

4. Correlation of nutrient intake between FFQ1 and FFQ2 Table 5 shows the results of Pearson correlation coefficients for unadjusted and energy-adjusted nutrient intake between FFQ1 and FFQ2. The nutrient intake was expressed as log transformed. The correlation coefficients ranged from 0.61 to 0.65 for carbohydrate, fat, protein, fiber and energy and from 0.20 to 0.63 for vitamins including carotene, vitamin A, thiamin, riboflavin, niacin, vitamin C and vitamin E. The correlation coefficients for sodium, calcium, iron, zinc, selenium was 0.47, 0.52, 0.56, 0.58, and 0.70, respectively, and for cholesterol, SFA, MUFA and PUFA 0.55, 0.61, 0.62, and 0.69, respectively. After adjusting for energy intake, the correlation coefficients of nutrient intake improved for protein, carbohydrate, fiber, carotene, vitamin C, sodium, calcium, zinc, selenium, cholesterol, SFA, and MUFA(from 0.33 to 0.73). For other nutrients including fat, vitamin A, thiamin, Vitamin E, and iron, no significant improvement was found after adjusting for energy.

10 ZHAO ET AL.

TABLE 6

Comparison of Food Consumption between FFQ1 and FFQ2 Based on Cross-classification of Quartiles

Food Group

Lowest Quartiles in FFQ1 (n=69) Highest Quartiles in FFQ1 (n=69)

Lowest Quartileson FFQ2 (%)

Lowest 2 Quartileson FFQ2 (%)

Highest Quartileson FFQ2 (%)

Highest Quartileson FFQ2 (%)

Highest 2 Quartileson FFQ2 (%)

Lowest Quartileson FFQ2 (%)

Rice 86.4 98.5 0.0 75.4 94.2 1.5Wheat Flour 81.4 97.1 1.4 83.8 100 0.0Other Cereals 71.6 95.5 0.0 82.4 94.1 4.4Potatoes 59.7 82.1 10.5 61.8 82.4 10.3Legume & Prod. 61.2 89.6 3.0 67.7 79.4 5.9Fresh Vegetables 55.7 81.1 9.0 47.1 73.5 5.9Salted Vegetables 52.24 73.5 11.9 50.0 73.5 10.3Fresh Fruit 61.2 82.1 9.0 63.2 79.4 8.8Nut 57.4 82.4 7.4 64.7 83.8 4.4Pork 62.7 88.1 4.5 66.2 83.8 4.4Poultry 50.8 94.1 1.6 67.7 79.4 8.8Fishes 73.5 92.7 0.0 67.7 83.8 4.4Egg 46.3 70.2 10.5 58.1 75.8 11.3Vegetable Oil 64.2 79.1 6.0 57.4 75.0 8.8Soy Sauce 72.7 83.3 9.1 78.9 90.4 3.9Salt 43.8 79.7 17.2 63.5 75.0 5.8Liquor 64.2 79.1 6.0 57.4 75.0 8.8

5. Cross-classification of food intakes between FFQ1 and FFQ2 Table 6 shows the cross classification of food consumption into quartiles in FFQ1 and FFQ2. Taking rice as example, 86.4% and 98.4% of the subjects falling under the lowest quartile in

Note. * P < 0.001.

TABLE 5

Pearson Correlation Coefficient of Nutrient Intake between FFQ1and FFQ2 (n=271)

Nutrients Crude Correlation* Energy-adjusted Correlation*

Energy 0.65 -Protein 0.63 0.63Fat 0.63 0.60Carbohydrate 0.61 0.61Fiber 0.64 0.65Carotene 0.43 0.52Vitamin A 0.58 0.37Thiamin 0.51 0.23Riboflavin 0.54 0.51Niacin 0.63 0.61Vitamin C 0.20 0.33Vitamin E Total 0.55 0.49Sodium 0.47 0.52Calcium 0.52 0.53Iron 0.56 0.42Zinc 0.58 0.63Selenium 0.70 0.72Cholesterol 0.55 0.57SFA 0.69 0.73MUFA 0.62 0.64PUFA 0.61 0.55

FFQ1 were classified into the lowest and lowest two quartiles in FFQ2, respectively; and 75.4% and 94.2% of the subjects falling under the highest quartile in FFQ1 was classified into the highest and highest two quartiles in FFQ2, respectively.


6. Cross-classification of nutrient intake between FFQ1 and FFQ2 Table 7 shows the cross-classification of nutrient intake (energy-adjusted) into quartiles in FFQ1 and FFQ2. Taking protein as example, 48.6% and 78.6% of the subjects falling under the lowest

TABLE 7

Comparison of Nutrients Intakes (Energy-adjusted) between FFQ1 and FFQ2 Based on Cross-classification of Quartiles

Nutrients

Lowest Quartiles in FFQ1 (n=69) Highest Quartiles in FFQ1 (n=69)

Lowest Quartilesin FFQ2 (%)

Lowest 2 Quartilesin FFQ2 (%)

Highest Quartilesin FFQ2 (%)

Highest Quartilesin FFQ2 (%)

Highest 2 Quartilesin FFQ2 (%)

Lowest Quartilesin FFQ2 (%)

EnergyProtein Fat Carbohydrate Fiber Carotene Vitamin A Thiamin Riboflavin Niacin Vitamin C Vitamin E Total Sodium Calcium Iron Zinc SeleniumCholesterolSFAMUFAPUFA

65.748.652.958.660.044.360.041.451.450.051.451.455.755.747.160.067.154.360.061.451.4

85.178.682.977.181.475.681.474.372.980.077.180.078.680.068.690.087.188.685.788.680.0

6.05.75.77.14.38.64.37.17.15.710.04.314.35.712.91.41.44.31.41.45.7

68.264.862.059.254.950.763.439.453.560.646.550.746.556.345.157.859.160.674.760.663.4

84.980.383.183.183.180.384.547.973.280.374.780.371.883.177.576.183.380.388.787.380.3

6.19.95.61.47.511.34.236.67.09.97.011.38.55.64.29.96.19.91.45.614.1

TABLE 8

Agreement and Opposite (%) between Classification by Food Consumption Level in FFQ1 and FFQ2 (n=271)

quartile in FFQ1 were classified into the lowest and lowest two quartiles in FFQ2 respectively; and 64.8% and 80.3 % of the subjects falling under the highest quartile in FFQ1 were classified into the highest and highest two quartiles in FFQ2 respectively.

7. Agreement between classification by food consumption level in FFQ1 and FFQ2 Table 8 shows the agreement between classification by food consumption level in FFQ1 and FFQ2. The percentage of subjects classified into the same quartile by FFQ1 and FFQ2 was 67.2%, 72.0%, and 76.0% for other cereals, rice and wheat flour, respectively. The percentage of subjects classified into the same quartile in FFQ1 and FFQ2 was 52.4%, 52.0%, 45.4%, 46.9%, and 56.1% for potatoes, legume and products, fresh vegetables and fresh fruits, respectively. For pork, poultry, fishes, egg and milk, the percentage of the subjects classified into the same quartile in FFQ1 and FFQ2 was 56.5%, 49.5%, 60.9%, 48.0%, and 76.4%, respectively. For vegetable oil, soy sauce, salt and liquor, the percentage of subjects classified into same quartile in FFQ1 and FFQ2 was 48.3%, 57.2%, 30.3%, and 48.3%, respectively. The percentage of misclassification (subjects classified into the opposite quartile by FFQ1 and FFQ2) ranged from 0 (milk, beer and wine) to 5.5% (salted vegetables).

Food Group% of Subjects

Classified into The Same Quartile

% of Subjects Classified into

Opposite Quartile

Rice 72.0 0.4

Wheat Flour 76.0 0.4

Other Cereal 67.2 1.1

Potatoes 52.4 5.2

Legume & Prod. 52.0 2.2

Fresh Vegetables 45.4 3.7

Salted Vegetables 46.9 5.5

Fresh Fruit 56.1 4.4

Pork 56.5 2.2

Poultry 49.5 2.6

Fishes 60.9 1.1

( to be continued on the next page)

12 ZHAO ET AL.

TABLE 10

Comparison of Daily Food Consumption between the 24-hour Diet Recall and FFQ1 (x ± s, g/d)

Food Group 24-hour FFQ1 Mean Difference# % of Difference#

Rice 249.0 ± 137.5 265.5 ± 177.7 16.4* 6.6

Wheat Flour 135.1 ± 152.7 136.0 ± 182.0 1.0 0.7

Other Cereals 20.2 ± 28.9 22.2 ± 35.9 2.0 10.0

Potatoes 12.3 ± 12.1 19.4 ± 23.5 7.1** 57.6

Legume & Products 44.0 ± 45.7 59.7 ± 69.4 15.7** 35.5

Fresh Vegetable 263.9 ± 98.8 293.5 ± 127.9 29.6** 11.2

Salted Vegetables 19.8 ± 15.0 20.9 ± 20.0 1.0 5.2

Validity

1. Validation by comparing FFQ1 with the 24-hour recall

1.1 Comparison of food consumption between FFQ1 and the 24-hour recall The comparison of food consumption between FFQ1 and the 24-hour recall is shown in Table 11. There were no significant differences (P>0.05) in the mean consumption of wheat flour, other cereals, salted vegetables, fishes, vegetable oil, soy sauce, salt, and liquor between FFQ1 and the 24-hour recall. There were significant differences (P<0.05) in the mean consumption of food including rice, potatoes, legume and products, fresh vegetables, fresh fruits, nuts, and pork, poultry, egg and milk between FFQ1 and the 24-hour recall; and the range of the mean differences of food consumption was 0.1 (vegetable oil, salt, liquor) to 70.3 (fresh fruits) gram per day. The percentage


8. Agreement between classification by nutrient intake level in FFQ1 and FFQ2 The percentage of subjects classified into the same quartile in FFQ1 and FFQ2 ranged from 35.7% (thiamin) to 59.8% (energy). The percentage of subjects misclassified into opposite quartile in FFQ1 and FFQ2 ranged from 0.7% (PUFA) to 11.0% (vitamin E). See Table 9.

(continued)


Classified into The Same Quartile


Opposite Quartile

Egg 48.0 5.2

Milk 76.4 0.0

Vegetable oil 48.3 3.7

Soy sauce 57.2 3.0

Salt 30.3 5.2

Liquor 48.3 3.7

TABLE 9

Agreement and Opposite (%) between Classification by Energy-Adjusted Nutrient Intake Level in FFQ1 and FFQ2 (n=271)

Nutrients % of Subjects

Classified into Same Quartile

% of Subjects Classified into Opposite Quartile

Energy 59.8 3.0

Protein 48.1 3.9

Fat 49.5 2.8

Carbohydrate 49.1 2.1

Fiber 47.4 2.8

Carotene 41.3 5.0

Vitamin A 50.5 2.1

Thiamin 35.7 11.0

Riboflavin 44.9 3.5

Niacin 45.2 3.9

Vitamin C 40.3 4.2

Vitamin E total 45.2 3.9

Sodium 44.9 5.7

Calcium 50.2 2.8

Iron 38.5 4.2

Zinc 48.4 2.8

Selenium 52.0 1.9

Cholesterol 43.5 4.2

SFA 49.8 1.8

MUFA 51.6 3.5

PUFA 54.4 0.7

of difference in the mean food consumption was equal to or less than 10% for rice, wheat flour, salted vegetables, vegetable oil, soy sauce, salt, and liquor. For potatoes, legume and products, fresh vegetables, fresh fruits, nuts, pork, poultry fishes, egg, milk and liquor, the difference was greater than 10%.


1.2 Comparison of nutrient intake between FFQ1 and the 24-hour recall The comparison of nutrient intake between FFQ1 and the 24-hour recall is shown in Table 11. There were no significant differences in the mean intake of fat, vitamin A, SFA, MUFA and PUFA between FFQ1 and the 24-hour recall. There were significant differences in the mean intake of all other nutrients including energy, protein, carbohydrate, fiber, carotene,

Note. #: Mean difference = (FFQ1-24-hour recall); % of difference = Mean difference / 24-hour recall; *P<0.05; **P<0.01; ***P<0.001.

TABLE 11

Comparison of Daily Nutrient Intake between the 24-hour Recall and FFQ1 (x ± s)

Nutrients 24-hour FFQ1 Mean Difference# % of Difference#

Energy (KJ/d) 9936.0 ± 1991.8 10885.9 ± 2701.1 949.9*** 9.6Protein (g/d) 77.7 ± 21.1 83.4 ± 24.0 5.7** 7.3Fat (g/d) 79.0 ± 24.3 78.5 ± 29.0 -0.5 -0.6Carbohydrate (g/d) 336.8 ± 77.0 380.9 ± 106.1 44.1*** 13.1Fiber (g/d) 10.2 ± 3.6 12.8 ± 5.4 2.6*** 25.0Carotene (mg/d) 2185.5 ± 1171.5 2645.5 ± 1435.34 460.0*** 21.1Vitamin A (mg/d) 257.5 ± 283.7 277.3 ± 406.7 19.8 7.7Thiamin (mg/d) 1.2± 0.3 1.3 ± 0.4 0.2*** 16.5Riboflavin (mg/d) 1.0 ± 0.3 1.2 ± 0.4 0.2*** 15.0Niacin (mg/d) 16.3 ± 5.1 17.4 ± 5.8 1.2** 7.2Vitamin C (mg/d) 76.7 ± 29.3 92.0 ± 38.1 15.2*** 19.9Vitamin E (mg/d) 27.7 ± 9.3 31.7 ± 11.2 4.1*** 14.6Sodium (mg/d) 5920.2 ± 1768.0 6285.2 ± 2392.6 365.1** 6.2Calcium (mg/d) 525.4 ± 204.0 596.1 ± 263.6 70.7*** 13. 5Iron (mg/d) 22.8 ± 6.0 24.2 ± 7.5 1.4** 6.1Zinc (mg/d) 12.0 ± 3.4 13.1 ± 3.6 1.1*** 9.0Selenium (mg/d) 46.5 ± 14.9 49.3± 19.5 2.7* 5.9Cholesterol (mg/d) 344.6 ± 206.1 374.3 ± 220.4 29.6* 8.6SFA (g/d) 19.9 ± 8.2 20.1 ± 9.4 0.1 0.7MUFA (g/d) 33.4 ± 12.6 33.0 ± 13.7 -0.4 -1.2PUFA (g/d) 20.9 ± 7.9 21.9 ± 10.1 1.0 5.0

Note. #: Mean difference = (FFQ1-24-hour recall); % of difference = Mean difference / 24-hour recall. *P<0.01; **P<0.001.


Fresh Fruits 137.7 ± 130.2 208.0 ± 150.7 70.3** 51.1

Nuts 6.0 ± 9.0 18.8 ± 27.7 12.8** 13.5

Pork 89.9 ± 56.0 72.5 ± 56.4 -17.4** 19.3

Poultry 21.7 ± 23.5 11.6 ± 13.6 -10.1** -46.8

Fishes 52.3 ± 47.2 57.7 ± 58.7 5.4 10.3

Egg 31.5 ± 20.6 42.2 ± 31.2 11.1** 35.1

Milk 24.4 ± 53.5 44.9 ± 131.5 20.5* 83.8

Vegetable Oil 34.6 ± 14.0 34.7 ± 15.8 0.1 0.29

Soy Sauce 12.7 ± 8.2 13.4 ± 9.9 0.7 5.5

Salt 9.3 ± 3.5 9.2 ± 4.7 -0.1 -1.0

Liquor 34.6 ± 14.0 34.7 ± 15.8 0.1 0.3

(continued)

thiamin, riboflavin, niacin, vitamin C and vitamin E, sodium, calcium, iron, zinc, selenium, and cholesterol. The percentage of difference was equal to or less than 10% for energy, protein, vitamin A, niacin, sodium, iron, zinc, selenium, cholesterol, SFA, MUFA, and PUFA. For carbohydrate, fiber, carotene, thiamin, riboflavin, vitamin C, vitamin E, and calcium, the differences of the intake in percentage was greater than 10%.

14 ZHAO ET AL.

1.3 Correlation of food consumption between FFQ1 and the 24-hour recall The Spearman correlation of food consumption between FFQ1 and the 24-hour recall is presented in Table 12. Correlation coefficient was 0.87, 0.80, and 0.59 for rice, wheat flour and other cereals, respectively; 0.32 for fresh vegetables, 0.40 for fresh fruits, and 0.36 for legume and products; 0.58 for pork, 0.53 for poultry, and 0.69 for fishes; 0.26 for egg and 0.68 for milk; 0.57 for liquor; 0.57 for vegetable oil, 0.69 for soy sauce and 0.49 for salt, respectively. 1.4 Correlation of nutrient intake between FFQ1 and the 24-hour recal l Table 13 shows Pearson correlation coefficients for unadjusted, energy-adjusted, attenuation-adjusted, energy and attenuation-adjusted nutrient intake between FFQ1 and the 24-hour recall. The nutrient intake was expressed as log transformed. The unadjusted correlation coefficients ranged from 0.33 (vitamin E and iron) to 0.63 (PUFA). After the nutrient intake was adjusted for energy, the correlation coefficients ranged from 0.28 ( iron) to 0.71 (SFA). The correlation coefficients was from 0.40 (vitamin E) to 0.77 (SFA) after the attenuation-adjustment. The correlation coefficients ranged from 0.27 (thiamin) to 0.86 (SFA) after adjusting for both energy and attenuation. The result shows that the correlation coefficient for most nutrients were improved after the adjustment. Note. #P<0.001; *nuts P< 0.05.

TABLE 12

Spearman Correlation Coefficient of Food Consumption between the 24-hour Recall and FFQ1 (n=271)

Food Group Correlation Coefficient#

Rice 0.87

Wheat Flour 0.80

Other Cereals 0.59

Potatoes 0.30



Salted Vegetables 0.32

Fresh Fruits 0.40

Nuts 0.12*

Pork 0.58

Poultry 0.50

Fishes 0.69

Egg 0.26

Milk 0.68

Vegetable Oil 0.57

Soy Sauce 0.69

Salt 0.49

Liquor 0.57


TABLE 13

Pearson Correlations of Nutrient Intake between 24-hour Recall and FFQ1 (n=271)

Nutrients Unadjusted* Energy Adjusted* Attenuation Adjusted Energy and Attenuation Adjusted

Energy 0.51 - 0.61 0.61

Protein 0.42 0.51 0.51 0.61

Fat 0.49 0.57 0.61 0.71

Carbohydrate 0.53 0.42 0.63 0.50

Fiber 0.35 0.31 0.44 0.39

Carotene 0.54 0.48 0.70 0.63

Vitamin A 0.43 0.38 0.58 0.51

Thiamin 0.35 0.22 0.44 0.27

Riboflavin 0.43 0.46 0.55 0.59

Niacin 0.42 0.35 0.52 0.43

Vitamin C 0.35 0.36 0.46 0.47

Vitamin E 0.33 0.38 0.40 0.47

Sodium 0.43 0.52 0.49 0.59

Calcium 0.50 0.52 0.64 0.67


Nutrients Unadjusted* Energy Adjusted* Attenuation Adjusted Energy and Attenuation Adjusted

Iron 0.33 0.28 0.43 0.36

Zinc 0.45 0.57 0.54 0.68

Selenium 0.53 0.57 0.66 0.72

Cholesterol 0.44 0.45 0.56 0.58

SFA 0.63 0.71 0.77 0.86

MUFA 0.54 0.58 0.65 0.70

PUFA 0.55 0.57 0.64 0.67

( continued )

Note. *P<0.001.

TABLE 14

Comparison of Food Consumption between FFQ1 and the Mean of the 18-day 24-hour Recall Based on Cross-classification into Quartiles

Food Group

Lowest Quartile in 24-hour Recall (n=69)

HighestQuartile in 24-hour Recall (n=69)

Lowest Quartilein FFQ1 (%)

Lowest 2 Quartiles in FFQ1 (%)

Highest Quartilein FFQ1(%)

Highest Quartile in FFQ1 (%)

Highest 2 Quartiles in FFQ1 (%)

Lowest Quartilein FFQ1(%)

Rice 83.6 100.0 0.0 64.7 91.2 0.0

Wheat Flour 65.7 91.0 0.0 86.8 98.5 1.5

Other Cereals 37.8 73.3 2.2 73.9 87.0 2.9

Potatoes 37.3 62.7 13.4 37.1 67.1 8.6

Legume & Prod. 51.5 73.5 19.1 35.8 70.2 12.0

Fresh Vegetables 47.0 74.2 19.7 38.8 82.1 3.0

Salted Vegetables 34.8 66.7 11.6 44.1 72.1 11.8

Fresh Fruit 42.7 70.59 5.9 37.3 64.2 11.9

Nut 30.5 55.2 21.9 28.8 53.4 20.6

Pork 59.1 84.9 6.1 53.7 86.6 4.5

Poultry 65.7 80.6 6.0 42.5 71.2 4.1

Fishes 72.7 92.4 1.5 56.7 83.6 1.5

Eggs 33.8 61.8 16.2 36.6 64.8 9.9

Vegetable Oil 57.3 84.0 8.0 65.0 88.3 6.7

Soy Sauce 76.2 88. 9 3.2 46.6 87.5 6.8

Salt 39.4 64.8 4.2 41.3 81.3 13.8

Liquor 57.3 84.0 8.0 65.0 88.3 6.7

1.5 Cross-classification of food consumption between FFQ1 and the 24-hour recall Table 14 shows the results of cross-classification of food consumption into quartiles in FFQ1 and the 24-hour recall. Taking rice as example, 83.6% and 100% of the subjects falling under the lowest

quartile in the 24-hour recall were classified into the lowest and lowest two quartiles in FFQ1; and 64.7% and 91.2% of the subjects falling under the highest quartile in the 24-hour recall were classified into the highest and highest two quartiles in FFQ1.

16 ZHAO ET AL.

1.6 Cross-classification of nutrient intake between FFQ1 and the 24-hour recall Table 15 shows the cross-classification of nutrient intake into quartiles in FFQ1 and the 24-hour recall. Taking protein as example, 50.0% and 75.7% of the subjects falling under

the lowest quartile in the 24-hour recall were classified into the lowest and lowest two quartiles in FFQ1; and 57.8% and 83.1% of the subjects falling under the highest quartile in 24-hour recall were classified into the highest and highest two quartiles in FFQ1.

TABLE 15

Comparison of Nutrient Intakes (Energy- adjusted) between FFQ1 and the Mean of the 18-day 24-hour Recalls Based on Cross-classification into Quartiles

Nutrients

Lowest Quartile in 24-hour Recall (n=69)

HighestQuartile in 24-hour Recall (n=69)



Highest Quartilein FFQ1 (%)


Highest 2 Quartile in FFQ1 (%)


Energy 46.4 76.8 11.6 49.3 76.8 7.3

Protein 50.0 75.7 7.1 57.8 83.1 4.2

Fat 52.9 82.9 5.7 59.15 87.3 4.2

Carbohydrate 40.0 70.0 10.0 54.9 77.5 11.3

Fiber 51.4 74.3 15.7 47.9 81.7 8.5

Carotene 57.1 85.7 5.7 49.3 77.5 5.6

Vitamin A 51.4 74.3 8.6 42.3 67.6 8.5

Thiamin 38.6 64.3 15.7 31.0 62.0 7.0

Riboflavin 55.7 85.7 5.7 43.7 73.3 5.6

Niacin 42.9 68.6 11.4 45.1 74.7 8.5

Vitamin C 40.0 77.1 8.6 36.6 70.4 11.3

Vitamin E Total 48.6 75.7 10.0 42.3 73.2 8.5

Sodium 40.0 75.7 11.4 52.1 78.9 4.2

Calcium 48.6 78.6 5.7 53.5 78.9 7.0

Iron 45.7 70 11.43 38.03 59.16 16.9

Zinc 62.9 91.4 5.7 54.9 81.7 4.2

Selenium 60.0 87.1 2.9 59.2 90.1 4.2

Cholesterol 51.4 74.3 12.9 45.1 78.9 2.8

SFA 61.4 90.0 1.4 64.8 100.0 0.0

MUFA 61.4 87.1 5.7 50.7 81.7 1.4

PUFA 52.9 81.4 10.0 62.0 87.3 7.0

1.7 Agreement between classification of food consumption level by FFQ1 and the 24-hour recall Table 16 shows the agreement between classification of food consumption level in FFQ1 and the 24-hour recall. The percentage of subjects classified into the same quartile by the 24-hour recall and FFQ1 ranged from 26.2% (nuts) to 74.4% (milk). The percentage of subjects classified into the opposite quartile by the 24-hour recall and FFQ1 ranged from 0 (rice, milk) to 14.0% (nuts).

1.8 Agreement between classification of nutrient intake level by FFQ1 and the 24-hour recall Table 17 shows the results of agreement between classification of nutrient intake level by FFQ1 and the 24-hour recall. The percentage of subjects classified into the same quartile by the 24-hour recall and FFQ1 ranged from 33.2% (vitamin C) to 47.3% (PUFA). The percentage of subjects classified into the opposite quartile in the 24-hour recall and FFQ1 ranged from 0.4% (PUFA) to 7.1% (iron and cholesterol).


TABLE 16

Agreement and Opposite (%) between Classification of Food Consumption Level by the 24-hour Recall and FFQ1 (n=271)




Opposite Quartile

Rice 62.7 0.0


Other Cereals 46.1 1.5

Potatoes 30.3 5.5

Legume & Prod. 35.8 7.8




Nuts 26.2 14.0

Pork 46.9 2.6

Poultry 42.1 2.6

Fishes 52.0 0.7

Eggs 31.4 6.6

Milk 74.4 0.0

Vegetable Oil 48.3 3.7

Soy Sauce 57.2 3.0

Salt 30.3 5.2

Liquor 48.3 3.7

TABLE 17

Agreement and Opposite (%) between Classifications of Energy-adjusted Nutrient Intake Level by the 24-hour Recall and FFQ1 (n=271)

Nutrients% of Subjects



Opposite Quartile

Energy 40.2 4.8

Protein 43.1 2.8

Fat 43.5 2.5


Fiber 42.8 6.0

Carotene 43.8 2.8

Vitamin A 38.9 4.2

Thiamin 27.6 5.7

Riboflavin 39.2 2.8

Niacin 37.5 5.0

Vitamin C 33.2 5.0

Vitamin E 38.9 4.6

Sodium 35.3 3.9

Calcium 43.5 3.2

Iron 34.6 7.1

Zinc 49.8 2.5

Selenium 48.8 1.8


SFA 44.2 1.8

MUFA 40.3 3. 9

PUFA 47.3 0.4

2. Validation by comparing FFQ2 with the 24-hour recall

2.1 Comparison of food consumption between FFQ2 and the 24-hour recall The comparison of food consumption between FFQ2 and the 24-hour recall is shown in Table 18. There were no significant differences (P>0.05) in mean consumption of other cereals, salted vegetables, fish, poultry, egg, milk, and liquor between 24-hour recall and FFQ2. There were significant differences (P<0.05) in the mean consumption of food including rice, wheat flour,

TABLE 18

Comparison of Daily Food Consumption between the 24-hour Recall and FFQ2 (x ± s, g/d)


Rice 249.0 ± 137.5 273.0 ± 171.6 24.0** 9.6

Wheat Flour 135.1 ± 152.7 146.9 ± 193.0 11.8* 8.7

Other Cereals 20.2 ± 28.9 21.2 ± 32.1 1.0 5.0


potatoes, legume and products, fresh vegetables, fresh fruits, nuts, and pork between FFQ2 and the 24-hour recall; and the range of mean differences of food consumption was 0.3 (fishes) to 57.6 (fresh vegetables) gram per day. The percentage of differences in the mean food consumption was from 8.7% (wheat flour) to 209.7% (nuts).

18 ZHAO ET AL.

TABLE 19

Comparison of Daily Nutrient Intake between the 24-hour Recall and FFQ2 (x ± s)


Energy (KJ/d) 9936.0 ± 1991.8 11020.6 ± 2591.7 1084.6** 10.9

Protein (g/d) 77.8 ± 21.1 83.7 ± 23.3 6.0** 7.7

Fat (g/day) 79.0 ± 24.3 76.2 ± 26.4 -2.8 -3.6

Carbohydrate (g/d) 336.8 ± 77.0 396.0 ± 106.6 59.3** 17.6

Fiber (g/d) 10.2 ± 3.6 12.6 ± 5.1 2.3** 22.8

Carotene (mg/d) 2185.5± 1171.5 2565.8 ± 1143.5 380.3** 17.4

Vitamin A (mg/d) 257.5 ± 283.7 247.9 ± 239.4 -9. 6 -3.7

Thiamin (mg/d) 1.2 ± 0.3 1.3 ± 0.3 0.1** 10.4

Riboflavin (mg/d) 1.0 ± 0.3 1.2 ± 0.4 0.2** 15.0

Niacin (mg/d) 16.3 ± 5.1 17.2 ± 5.6 1.0* 5.9

Vitamin C (mg/d) 76.7 ± 29.3 91.7 ± 35.6 15.0** 19.6

Vitamin E total (mg/d) 27.7 ± 9.3 30.8 ± 10.4 3.2** 11.5

Sodium (mg/d) 5920.2 ± 1768.0 6099.3 ± 1755. 9 179.1* 3.0

Calcium (mg/d) 525.4 ± 204.0 587.3 ± 240.6 61.9* 11.8

2.2 Comparison of nutrients intake between FFQ2 and the 24-hour recall The comparison of nutrients intake between FFQ2 and the 24-hour recall is shown in Table 19. There were no significant differences in the mean intake of fat, vitamin A, iron, cholesterol, SFA, MUFA and PUFA between the 24-hour recall and FFQ2.

There were significant differences in the mean intake of all other nutrients including energy, protein, carbohydrate, fiber, carotene, riboflavin, niacin, vitamin C and vitamin E, sodium, calcium, zinc and selenium; and the range of the difference in percentage was from 3.0% (sodium) to 19.6% (vitamin C).

Note. #: Mean difference = (FFQ2-24-hour recall); % of difference = Mean difference / 24-hour recall; *P<0.01; **P<0.001.


Potatoes 12.3 ± 12.1 20.0 ± 18.9 7.7* 62.5

Legume & Products 44.0 ± 45.7 56.4 ± 55.0 12.4* 28.1

Fresh Vegetable 263.9 ± 98.8 298.5 ± 117.5 34.6* 13.1

Salted Vegetables 19.8 ± 15.0 19.2 ± 14.2 -0.6 -3.2

Fresh Fruits 137.7 ± 130.2 195.2 ± 134.9 57.6* 41.8

Nuts 6.0 ± 9.0 18.6 ± 24.3 12.6* 209.7

Pork 89.9 ± 56.0 71.8 ± 56.2 -18.1* -20.2

Poultry 21.7 ± 23.5 10.7 ± 13.3 -11.0 -50.6

Fishes 52.3 ± 47.2 52.7 ± 56.0 0.3 0.7

Egg 31.5 ± 20.6 39.4 ± 28.4 7.9 25.1

Milk 24.4 ± 53.5 48.7 ± 104.7 24.3 99.6

Liquor 34.6 ± 14.0 33.3 ± 13.5 -1.3 3.8

( continued )



TABLE 20

Spearman Correlations between the 24-hour Recall and FFQ2 (n=271)

Food Group Correlation Coefficient*

Rice 0.85

Wheat Flour 0.85

Other Cereals 0.55

Potatoes 0.30



Salted Vegetables 0.33

Fresh Fruits 0.47

Nuts 0.15

Pork 0.67

Poultry 0.53

Fishes 0.71

Egg 0.35

Milk 0.72

Liquor 0.90

Note. *P<0.001.


Iron (mg/d) 22.8 ± 6.0 23.6 ± 7.1 0.8 3.6

Zinc (mg/d) 12.0 ± 3.4 12.7 ± 3.5 0.7* 5.7

Selenium (mg/d) 46.5 ± 14.9 49.3 ± 18.6 2.7* 5.9

Cholesterol (mg/d) 344.6 ± 206.1 351.4 ± 211.5 6.8 2.0

SFA (g/d) 19.9 ± 8.2 19.4 ± 9.0 -0.5 -2.7

MUFA (g/d) 33.4 ± 12.6 32.4 ± 13.4 -1.0 -3.0

PUFA (g/d) 20.9 ± 7.9 21.0 ± 8.5 0.1 0.5

Note. #: Mean difference = (FFQ2-24-hour recall); % of difference = Mean difference / 24-hour recall; *P<0.01; **P<0.001.

2.3 Correlation of food consumption between FFQ2 and the 24-hour recal The Spearman correlation of food intakes between FFQ2 and the 24-hour recall is presented in Table 20. Correlation coefficient was 0.85, 0.85, and 0.55 for rice, wheat flour and other cereals, respectively; 0.38 for fresh vegetables, 0.47 for fresh fruits, 0.46 for legume and products; 0.67 for pork, 0.53 for poultry, 0.71 for fishes; 0.35 for egg , 0.72 for milk; 0.90 for liquor. For edible oil, soy sauce and salt, the consumption was estimated based on the family consumption data, and the same time period was covered for the 24-hour recall and FFQ2, so the correlation coefficient was not included in the analysis.

( continued )

2.4 Correlation of nutrient intake between FFQ2 and the 24-hour recall Table 21 shows Pearson correlation coefficients for unadjusted, energy-adjusted, attenuation-adjusted, energy and attenuation-adjusted daily nutrient intake between FFQ2 and the 24-hour recall. The nutrient intake was expressed as log transformed. The unadjusted correlation coefficients ranged from 0.22 (vitamin C) to 0.84 (PUFA). After the nutrient intake was adjusted for energy, the correlation coefficients was 0.32 for vitamin C and 0.85 for PUFA. The mean of correlation coefficients was from 0.23 (vitamin C) to 0.98 (PUFA) after adjusting for attenuation. The correlation coefficients was 0.23 for vitamin C and 0.99 for PUFA after adjusting for both energy and attenuation. The results showed that the correlation coefficient for most nutrients were improved after the adjustment.

TABLE 21

Pearson Correlations between the 24-hour Recall and FFQ2 (n=271)

Nutrients Crude Correlation* Energy Adjusted Correlation*

Attenuation Adjusted Correlation

Energy and Attenuation Adjusted Correlation

Energy 0.54 - 0.65 0.65

Protein 0.48 0.60 0.58 0.73


20 ZHAO ET AL.

Note. *P<0.001.

2.5 Cross-classification of food consumption between FFQ2 and the 24-hour recall Table 22 shows the cross-classification for food consumption into quartiles in FFQ2 and the 24-hour recall. Taking rice as example, 86.5% and 98.5% of the subjects falling under the

lowest quartile in the 24-hour recall was classified into the lowest and lowest two quartiles in FFQ2; and 69.1% and 86.8% of the subjects falling under the highest quartile in the 24-hour recall was classified into the highest and highest two quartiles in FFQ2.

TABLE 22

Comparison of Food Consumption between FFQ2 and the Mean of the 18-day 24-hour Recalls Based on Cross-classification into Quartiles

Food Group

Lowest Quartiles in 24-hour Recall (n=69)

HighestQuartiles in 24-hour Recall (n=69)







Rice 86.5 98.5 0.0 69.1 86.8 0.0

Wheat Flour 70.2 95.5 0.0 86.8 100.0 0.0

Other Cereals 33.3 75.6 3.3 69.6 85.51 7.3

Potatoes 37.3 64.2 13.4 42.9 70.00 11.4

Legume & Products 54.4 75.0 11.8 40.3 68.66 9.0

Fresh Vegetables 40.9 75.8 12.1 37.3 73.13 10.5

Salted Vegetables 36.2 71.0 15.9 41.2 70.59 11.8

Fresh Fruit 41.2 72.1 5.9 46.3 74.63 9.0

Nuts 27.6 54.3 20.0 32.9 58.91 21.9

Nutrients Crude Correlation* Energy Adjusted Correlation*

Attenuation Adjusted Correlation

Energy and Attenuation Adjusted Correlation

Fat 0.68 0.78 0.85 0.97

Carbohydrate 0.56 0.66 0.67 0.78

Fiber 0.45 0.38 0.57 0.48

Carotene 0.40 0.43 0.52 0.55

Vitamin A 0.50 0.32 0.67 0.43

Thiamin 0.46 0.47 0.58 0.59

Riboflavin 0.44 0.48 0.56 0.61

Niacin 0.50 0.48 0.62 0.60

Vitamin C 0.22 0.32 0.23 0.41

Vitamin E Total 0.67 0.68 0.82 0.82

Sodium 0.83 0.69 0.95 0.80

Calcium 0.47 0.48 0.60 0.61

Iron 0.44 0.30 0.57 0.39

Zinc 0.58 0.72 0.70 0.86

Selenium 0.56 0.62 0.70 0.78

Cholesterol 0.57 0.61 0.72 0.77

SFA 0.70 0.78 0.85 0.95

MUFA 0.76 0.79 0.92 0.95

PUFA 0.84 0.85 0.98 0.99

( continued )



TABLE 23

Comparison of Nutrient Intake (Energy-adjusted) between FFQ2 and the Mean of the 18-day 24-hour Recalls Based on Cross-classification into Quartiles

Nutrients

LowestQuartile in 24-hour Recalls (n=69)

HighestQuartile in 24-hour Recalls (n=69)


Lowest 2 Quartile in FFQ2 (%)



Highest 2 Quartile in FFQ2 (%)


Energy 47.8 79.7 4.4 49.3 75.4 4.4

Protein 52.9 82.9 2.9 70.4 87.3 4.2

Fat 62.9 91.4 1.4 69.0 91.6 0.0

Carbohydrate 57.1 80.0 10.0 59.2 85.9 1.4

Fiber 50.0 78.6 7.1 56.3 88.7 2.8

Carotene 47.1 71.4 8.6 43.7 71.8 12.7

Vitamin A 55.7 80.0 11.4 49.3 76.1 9.9

Thiamin 42.9 72.9 4.3 49.3 80.3 8.5

Riboflavin 44.3 70.0 11.4 53.5 84.5 2.8

Niacin 51.4 80.0 5.7 52.1 80.3 4.2

Vitamin C 37.1 68.5 10.0 42.3 69.0 15.5

Vitamin E Total 64.3 91.4 1.4 53.5 84.5 1.4

Sodium 62.9 84.3 7.1 56.3 84.5 4.2

Calcium 45.7 72.9 10.0 52.1 83.1 8.5

Iron 55.7 84.3 8.6 36.6 62.0 12.7

Zinc 67.1 91.4 2.9 64.8 87.3 2.8

Selenium 67.2 92.5 3.0 67.7 94.1 0

Cholesterol 57.1 84.3 2.9 50.7 84.5 1.4

SFA 61.4 87.1 1.4 70.4 97.2 0.0

MUFA 70.0 94.3 0.0 64.8 97.2 1.4

PUFA 71.4 95.7 1.4 76.1 97.2 0.0

2.6 Cross-classification of nutrient intake between FFQ2 and the 24-hour recall Table 23 shows the cross-classification for nutrients intake into quartiles in the 24-hour recall and FFQ2. Taking cholesterol as example, 57.1% and 84.3% of the subjects falling under

the lowest quartile in the 24-hour recall were classified into the lowest and lowest two quartiles in FFQ2; and 50.7% and 84.5% of the subjects falling under the highest quartile by the 24-hour recall were classified into the highest and highest two quartiles by FFQ2.

Food Group

Lowest Quartiles in 24-hour Recall (n=69)

HighestQuartiles in 24-hour Recall (n=69)







Poultry 53.7 91.0 4.5 48.0 78.09 5.5

Fishes 66.7 87.9 1.5 55.2 92.53 1.5

Egg 39.7 66.2 11.8 40.9 67.61 8.5

Liquor 100.0 100.0 0.0 100.0 100 0.0

( continued )

22 ZHAO ET AL.

TABLE 24

Agreement and Opposite (%) between Classifications of Food Consumption Level by the 24-hour Recall and FFQ2 (n=271)




Opposite Quartile

Rice 63.8 0.0


Other Cereals 40.6 3.0

Potatoes 34.0 6.3

Legume & Products 40.6 5.2




Pork 45.4 0.4

Poultry 42.1 2.6

Fishes 48.3 0.7

Egg 31.0 5.2

Milk 69.7 0.0

Liquor 100.0 0.0

2.7 Agreement between classification of food consumption level in FFQ2 and the 24-hour recall Table 24 shows the agreement between classification of food consumption level in FFQ2 and the 24-hour recall. The percentage of subjects classified into the same quartile in the 24-hour recall and FFQ2 ranged from 31.0% (egg) to 100% (liquor). The percentage of subjects classified into the opposite quartile by the 24-hour recall and FFQ2 ranged from 0 (rice and liquor) to 7.0% (salted vegetables).

2.8 Agreement between classification of nutrient intake level in FFQ2 and the 24-hour recall Table 25 shows the resul ts of agreement between classification of nutrient intake level in FFQ2 and the 24-hour recall. The percentage of subjects classified into the same quartile in the 24-hour recall and FFQ2 ranged from 31.5% (vitamin C) to 53.0% (PUFA). The percentage of subjects classified into the opposite quartile in 24-hour recall and FFQ2 ranged from 0.4% (fat, SFA and PUFA) to 6.4% (vitamin C).

TABLE 25

Agreement and Opposite (%) between Classification of Energy-adjusted Nutrient Intake Level by the 24-hour Recall and FFQ2 (n=271)

Nutrients % of Subjects Classified

into Same Quartile


Opposite Quartile

Energy 39.9 2.2

Protein 48.1 1.8

Fat 51.6 0.4


Fiber 43.8 2.5

Carotene 37.5 5.3

Vitamin A 41.7 5.3

Thiamin 36.0 3.2

Riboflavin 41.0 3.5

Niacin 43.5 2.5

Vitamin C 31.5 6.4

Vitamin E Total 46.0 0.7

Sodium 48.4 2.8

Calcium 43.8 4.6

Iron 37.5 5.3

Zinc 52.6 1.4

Selenium 42.1 3.0


SFA 51.2 0.4

MUFA 42.1 1.1

PUFA 53.0 0.4

DISCUSSION

The complexity and variability of diet among free-living people make eating behaviors difficult to ascertain. All dietary assessment methods have their limitations and do not necessarily provide a true and absolute estimate of food consumption. The techniques employed in this study, i.e. the 24-hour recall and FFQ are the most commonly used methods for the assessment of food and nutrient intake. The purpose of this study was to estimate the reproducibility of FFQ and its validity by comparing with the 18-day 24-hour recall among 271 healthy Chinese adults who lived either in urban or rural areas of China. FFQ was developed by the study group and designed according to the Chinese dietary pattern.


Reproducibility

In conducting a reproducibility evaluation, it is unrealistic to administer the questionnaire to cover a very short interval, such as a few days or weeks. When a longer interval of time is used, true changes in dietary intake, as well as variation in response, may lead to the reduction of reproducibility[6]. This study used 1 year as the interval between the two interviews which was the most desirable interval used in other studies.

The reproducibility of FFQ in this study was evaluated in three aspects between FFQ1 and FFQ2, i.e. comparison of the mean intake of foods and nutrients; correlation analysis of their intake; and cross-classification and agreement on their intake. A high degree of reproducibility was shown for both food consumption and nutrient intake. Except for wheat flour and fishes, there were no significant differences in the mean consumption of all other major foods including rice, other cereals, fresh vegetables, salted vegetables, fresh fruits, nuts, pork, poultry, egg, milk, vegetable oil, soy sauce, salt and liquor (Table 3); and it is the same for all major nutrients except thiamin (Table 4). The correlation coefficients ranged from 0.43 to 0.90 for foods consumption and 0.23 to 0.73 for nutrients intake (Tables 5 and 6).

The reproducibility of food frequency questionnaires has been examined in a var ie ty of s tudies . Reproducibility in other studies does not differ from ours since wide ranges from 0.20 to 0.80 are found[11, 23-27]. In a smaller number of studies, the reproducibility of specific food items has been examined. The repeated correlations have been considerably weaker for nutrient intake as compared with those for food consumption. Among 323 American men and women interviewed at an interval of 6 to 10 years, Byers and his colleagues (1987) found average correlation coefficients of 0.41 for vegetables, 0.41 for fruits, 0.53 for dairy products, and 0.39 for meats. Colditz and his coworkers (1987) compared frequencies of foods reported by 1 497 women at an interval of approximately 9 months. Correlations were highest for beverage (r=0.70) and they ranged from 0.60 to 0.70 for foods eaten frequently and from 0.34 to 0.45 for foods eaten infrequently[6]. In summary, the reproducibility in our study is generally consistent with the results repeated by other investigators.

Validity

Relative validity was tested by comparing the results of food consumption and nutrient intake from

both FFQ1 and FFQ2 with those from the average of the 18 days 24-hour recall. The correlation between FFQ2 and the average of the 18 days 24-hour recalls were generally stronger than that with FFQ1. The relative validity of FFQ1 was performed in the absence of the possible bias due to a learning effect in FFQ2. This was closer to the real situation where subjects were deprived of any previous experience in quantifying their diet. However, the relative validation of FFQ2 covered the same period as the 24-hour recall.

The unadjusted correlation coefficients between FFQ1 and the mean 24-hour recall ranged from 0.12 to 0.87 for food and from 0.33 to 0.63 for nutrients. The unadjusted correlation coefficients between FFQ2 and the 24-hour recall ranged from 0.33 to 0.85 for food and from 0.22 to 0.84 for nutrients. The strongest correlations were found in staple foods (rice and wheat flour), pork, poultry and fishes, milk, and liquor (Table 13, Table 21) which are consumed frequently and widely. The weakest correlation were found in foods which are not consumed regularly such as potatoes, nuts, legume, and products; and also in fresh vegetables which involves a large number of varieties. Adjustment for energy and for attenuation improved correlations for nutrients. It was ranged from 0.27 to 0.86 for FFQ1 and the 24-hour recall, and from 0.39 to 0.99 for FFQ2 and the 24-hour recall.

Among the available and feasible comparison methods for validating a FFQ, diet records are likely to have the least correlation with FFQ due to the restrictions imposed by a fixed list of foods, memory, perception of portion size, and interpretation of questions. These sources of error are minimally shared by diet records because diet records are open-ended, do not depend on memory (foods are recorded on a meal-by-meal basis), and allow direct assessment of portion size. The primary alternative for the use of diet records as a standard for evaluating a FFQ is the collection of multiple 24-hour recalls. Among the validation studies of FFQ with the 24-hour recall carried out so far, most show correlation with nutrients ranging from 0.04 to 0.89 (average 0.34 to 0.62)[28-33]. The results of an evaluation of relative validation depend on several factors mentioned by Block[34]. These factors include choice of reference method, the degree of homogeneity of intake values within the population, recall period, and the number of the days recorded. The standard method in our study was a six repeated 24-hour recall for three consecutive days, i.e. a totally 18-day 24-hour recall over a one-year period. Our study subjects were a group of adult residents with a fairly fixed lifestyle. These may partly account for the stronger correlations obtained in our study.

24 ZHAO ET AL.

By comparing the mean intake of food and nutrients between FFQ1 or FFQ2 and the 24-hour recall it was shown that there were significant differences between the FFQ1 and the 24-hour recall for most foods and nutrients, and this was also true for FFQ2 and the 24-hour recall. The consumption of rice, potatoes, legume and products, fresh vegetables, fresh fruits, eggs, and milk was shown to have been overestimated, while pork and poultry were underestimated by FFQ1 as compared with those by the 24-hour recall (Table 10). The percentage of difference in the mean food consumption was equal to or less than 10% for rice, wheat flour, salted vegetables, vegetable oil, soy sauce, salt, and liquor. For potatoes, legume and products, fresh vegetables, fresh fruits, nuts, pork, poultry fishes, egg, milk, and liquor, the difference was greater than 10%. In the case of nutrient intakes, energy, protein, carbohydrate, fiber, carotene, thiamin, riboflavin, niacin, vitamin C, vitamin E, sodium, calcium, iron, zinc, selenium, and cholesterol were all overestimated by FFQ1 (Table 11). The percentage of difference was equal to or less than 10% for energy, protein, vitamin A, niacin, sodium, iron, zinc, selenium, cholesterol, SFA, MUFA, and PUFA. For carbohydrate, fiber, carotene, thiamin, riboflavin, vitamin C, vitamin E, and calcium, the differences of the intake in percentage was greater than 10%. The consumption of rice, wheat flour, potatoes, legume and products, fresh vegetables, fresh fruits, nuts were overestimated, and pork was underestimated by FFQ2 (Table 18) as compared with those by the 24-hour recall. The percentage of difference in the mean food consumption was equal to or less than 10% for rice and wheat flour. For potatoes, legume and products, fresh vegetables, fresh fruits, nuts, pork, and poultry, the difference was greater than 10%. In the case of nutrient intake, energy, protein, carbohydrate, fiber, carotene, thiamin, riboflavin, niacin, vitamin C, vitamin E, sodium, calcium, zinc, and selenium were all overestimated by FFQ2 (Table 19). The percentage of difference was equal to or less than 10% for protein. For the intake of energy, carbohydrate, fiber, carotene, thiamin, riboflavin, vitamin C, vitamin E, and calcium, the percentage of differences was greater than 10%.

The results showed a trend that FFQ overestimated the mean intake for most of the food groups and nutrients included in the study. There were few studies to show the information on over-or underestimates of both food consumption and nutrient intake by FFQ and the 24-hour recall. The overestimates of both food and nutrient intake from our study may possibly be explained by that fact that estimates of food and nutrient intake by the 24-hour recall are derived directly from reports of actual diet of 18 days,

and that in the FFQ intake comes from summaries or averages of foods consumed during the year and using the 18 days 24-hour recall may be not long enough to estimate individual one year diet intake since diet variation exists cross the season and day to day. The results of high degree of reproducibility strongly support the assumption that FFQ could reflect the one year dietary information of the individual.

Despite some overestimation in both food cons-umption and nutrient intake by FFQ, agreement in classification is comparable to what other studies have shown[9,35-36]. Classification in the same quartile in our study shows a mean of over 45% agreement, and classification in the same and next quartile shows a mean agreement over 75%.

Finally, to confirm the validity of FFQ in China, it is desirable to perform 24 hour urinalysis: nitrogen for exact protein intake, K for vegetables and fruits, Na for salt and others on small groups in the future.

Application

FFQ validated in this study has been applied in several other studies in China, in which some food items are deleted and other new items are added according to the purpose and setting of the studies. For example, it was used in the “study on nutritional status of folate in Chinese adults aged 35 to 64 years” in China, in which more varieties of vegetables were added in order to obtain a more complete folate intake. It was concluded that FFQ was a feasible method to study the nutritional status of folate among Chinese adults. FFQ was also used in the study on “effectiveness of iron fortified soy sauce in controlling IDA in large populations”. In addition, in the 2002 National Nutrition and Health Survey in China a simplified FFQ based on this FFQ was used as one of the three parallel dietary assessment methods.

CONCLUSION

It has been frequently stated that no perfect measure of dietary intake is ever present. This indicates a continuous need to search for better gold standard method. The popularity of FFQ is due to its easiness of use and relatively low operational cost as compare to other methods. It measures long-term dietary intake and captures past dietary habits, which is often more relevant for studying relationship between diet and diseases than short-term current diet recall. In this study, the reproducibility and validity of the Chinese FFQ were all evaluated with satisfactory outcomes, suggesting that FFQ could be used to classify study subjects according to their food or nutrient intake over a one-year period. These findings


have confirmed that FFQ is an appropriate instrument to measure the usual food consumption, nutrient intake and dietary patterns of adult Chinese.

ACKNOWLEDGEMENTS

This study would not be possible without the funding from the National Institute for Cancer Research, NIH, USA, and the National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention.

The authors would like to express their great thanks: to the local team and all subjects for their great contribution and support; to Dr. Hai-Jiang LIAO, Ms. Yue YOU, Ms. Yi ZHAI, Ms. Xiao-Feng SHI, for their assistance in data management and processing.

REFERENCES

1. Ministry of Health. Health Statistic Yearbook of China, 1990-2000.

2. MacLennan R, Macrae F, Bain C, et al. (1995). Randomized trial of intake of fat, fiber, and beta carotene to prevent colorectal adenomas. The Australian Polyp Prevention Project. Journal of the National Cancer Institute 87(23), 1760-1766.

3. Miettinen M, Turpeinen O, Karvonen M J, et al. (1972). Effect of cholesterol-lowering diet on mortality from coronary heart-disease and other causes: a twelve-year clinical trial in men and women. Lancet 2, 835-838.

4. World Cancer Research Fund/American Institute for Cancer Research (1997). Scientific evidence and judgment. In Food, Nutrition and the Prevention of Cancer: a global perspective. pp 72-90.

5. Zhao W H, Chen J S (2001). Implications from and for food cultures for cardiovascular disease: diet, nutrition and cardiovascular diseases in China. Asia Pacific Journal of Clinical Nutrition 10(2), 146-152.

6. Willett, W C (1998). Nutritional epidemiology. New York and Oxford: Oxford University Press, pp. 50-51.

7. Gibson R S (1993). Nutritional Assessment: A Laboratory Manual. New York and Oxford: Oxford University Press, pp. 15-16.

8. Brown M L (1990). Present Knowledge in Nutrition. Washington, D. C., ILSI Nutrition Foundation, pp. 401-402.

9. Willett W C, Sampson L, Stampfer M J, et al. (1985). Reproducibility and validity of a semi- quantitative food frequency questionnaire. Am J Epidemiol 122, 51-65.

10. Rimm E B, Giovannucci E L, Stampfer M J, et al. (1992). Reproducibility and validity of a expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol 135, 1114-1126.

11. Van Liere M J, Lucas F, Clavel F, et al. (1997). Relative validity and reproducibility of a French dietary history questionnaire. Int J Epidemiol 26(suppl 1), s128-136.

12. GnardellwasC, Trichopoulou A, Katsouyanni K, et al. (1995). Reproducibility and validity of an extensive semi-quantitative food frequency questionnaire among Greek school teachers. Epidemiology 6, 74-77.

13. Block G, Woods M, Potosky A, et al. (1990). Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol 43, 1327-1335.

14. Goldbohm RA, van den Brandt PA, Brants HAM, et al. (1994). Validation of a dietary questionnaire used in a large-scale prospective cohort study on diet and cancer. Eur J Clin Nutr 48, 253-265.

15. Mannisto S, Virtanen M, Mikkonen T, et al . (1996). Reproducibility and validity of a food frequency questionnaire in a case-control study on breast cancer. J Clin Epidemiol 49, 401-409.

16. Willett W C (1994). Future directions in the development of food-frequency questionnaires. Am J Clin Nutr 59(suppl), 171s-174s.

17. Sasaki S, Yanagibori R, Amano K (1998). Self-Administered Diet History Questionnaire Developed for Health Education; A Relative Validation of The test-Version by Comparison with 3-Day Diet Record in Women. Journal of Epidemiology 8, 203-215.

18. Chen J (1991). Dietary practices and cancer mortality in China. IRAC Scientific Pub lications 105, 18-21.

19. Zhao W H, Hasegawa Kyoko, Chen J S (2002). The use of food frequency questionnaire for various purposes in China. Public Health Nutrition 5(6A), 829-833.

20. Shu X O, Zheng W, Potischman N, et al. (1993). A population-based case-control study of dietary factors and endometrial cancer in Shanghai, People’s Republic of China. Am J Epidemiol 137, 155-165.

21. Ge K, Zhai F, Yan H, et al. (1996). The overall plan of the 1992 National Nutrition Survey of China, sampling methods and survey methods. In: The dietary and nutritional status of Chinese population (1992 National Nutrition Survey). Vol. 1. Beijing, China: People’s Medical Publishing House. pp. 1-88.

22. Institute of Nutrition and Food Hygiene, Chinese Academy of Preventive Medicine (1991). Food Composition Table. Beijing: People’s Health Publishing House.

23. Rosner B, Willett W C (1988). Interval estimates for correlation coefficient corrected for within-person variation: implication for study design and hypotheswastesting. Am J Epidemiol 127, 377-386.

24. Beaton G H, Milner J, Corey P, et al. (1979). Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am J Cli Nutr 32, 2546-2559.

25. Jacbasen B K, Bönna K H. (1990). The repro-ducibility of dietary data from a self-administered questionnaire: The Tromsö study. Int J Epidemiol 19, 349-353.

26. Nomura A, Hankin J H, Rhoads G G (1976) . The reproducibility of dietary intake data in a prospective study of gastrointestinal cancer. Am J Clin Nutr 19, 1432-1436.

27. Salvini S, Hunter D J, Sampson L, et al. (1989). Food- based

26 ZHAO ET AL.

validation of a dietary questionnaire: the effects of week- to-week variation in food consumption. Int J Epidemiol 18, 856-867.

28. Block G, F E Thompson, A M Hartman, et al. (1992). Comparison of two Dietary Questionnaires validated against multiple dietary records collected during a 1-year period. J Am Diet Assoc 92, 686-693.

29. EPIC Group of Spain (1997). Relative Validity and reproducibility of a diet history questionnaire in Spain. I. Foods. Int J Epidemiol 26(suppl), s91-99.

30. EPIC Group of Spain (1997). Relative validity and reproducibility of a diet history questionnaire in Spain. II. Nutrients. Int J Epidemiol 26(suppl), s100-109.

31. Johansson I, G Hallmans (1995). Impact of photo portion Illustrations on Relative Validity of a Foods Frequency Questionnaire. University of Umea, Sweden. Submitted.

32. Katsouyanni K, E B Rimm C Gnardellis, D. Trichopoulos, et al. (1997). Reproducibility and relative validity of an

extensive semiquantitative food frequency questionnaire using dietary records and biochemical markers among Greek school teachers. Int J Epidemiol 26(suppl), s118-127.

33. Rockett H R, A M Wolf, G A Colditz (1995). Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. J Am Diet Assoc 95, 336-340.

34. Block G, Woods M, Potosky A, et al. (1990). Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol 43, 1327-1335.

35. Jain M G, Harrison L, Howe G R, et al. (1982). Evaluation of a self-administered dietary questionnaire using multiple diet records. J Clin Nutr 36, 931-935.

36. Pie t inen P, Har tman A M, Haapa E, et a l . (1988) . Reproducibility and relative validity of dietary assessment instruments. II. A qualitative food frequency questionnaire. Am J Epidemiol 128, 667-676.


A. Demographic CharacteristicsNameAddress

1. Individual code 2. Gender

(1) Male (2) Female3. Date of Birth4. Occupation

(1) farmers (2) blue collar workers(3) professionals (4) office staff, clerk(5) housework (6) retired(7) others

5. Labor intensity(1) very light (2) light(3) medium (4) heavy(5) very heavy

6. Education (1) primary school (2) middle school (3) high school (4) college(5) graduate school (6) illiteracy

7. Height (cm) Weight (kg)

B. Please recall your eating habits in the past one year1. Do you eat on schedule?

(1) not on schedule (2) largely on schedule(3) strictly on schedule

2. How fast are you eating?(1) slow (2) average(3) fast (4) very fast

3. Do you like your foods to be:(1) cold (2) warm(3) hot (4) very hot

4. How many meals do you usually have every day?5. How many days do you usually have breakfast per week?

C. Please recall your habits with spoiled foods1. Where do you keep your left-over in the summer?

(1) refrigerator (2) cooking pot(3) dishes

2. How many times per week do you eat left-over on the average?

(1) never (2) 1-2 times(3) 3-4 times (4) over 5 times

3. Do you re-heat left-over before eating?(1) never (2) occasionally(3) often (4) always

4. How do you deal with spoiled vegetables and fruits?(1) throw away completely(2) throw away the spoiled part

5. How do you deal with moldy rice, steam bun, bread

or other grain products?(1) throw away completely(2) throw away the moldy part

6. Do you eat food with strongly sour or bad smell?(1) never (2) occasionally(3) often

7. Have your grain or rice or other staple foods ever become moldy during storage?

(1) yes (2) noIf yes, please continue to answer the following questions:

7.1 How often does it happen?(1) seldom (2) occur in several years(3) occur every year

7.2 How do you handle moldy corn?(1) throw away completely(2) expose the corn to the sun and throw away the

moldy part(3) do not expose the corn to the sun and throw

away the moldy part(4) eat most of the corn

7.3 How do you handle moldy peanuts?(1) throw away completely (2) expose the peanuts to the sun and throw away

the moldy part(3) do not expose the peanuts to the sun and throw

away the moldy part(4) eat most of the peanuts

7.4 How do you handle moldy wheat?(1) throw away completely (2) expose the wheat to the sun and throw away the

moldy part(3) do not expose the wheat to the sun and throw

away the moldy part(4) eat most of the wheat

7.5 How do you handle moldy rice or other grains?(1) throw away completely (2) expose the grains to the sun and throw away

the moldy part(3) do not expose the grains to the sun and throw

away the moldy part(4) eat most of the grains

D. What is your source of drinking water? (1) tap water (2) well water(3) river or lake water

Appendix 1: Chinese Food Frequency Questionnaire

Date of interview year month day

28 ZHAO ET AL.

E1 Staple Foods

AverageIntake/Time

Frequency

Food CodeTime/Day Time /Week Time/Month Time/Year Never Eat

Liang Filling According to the Actually Aonsumed Filling 99

Rice

Wheat Flour

Stick Rice

Rice Noodle

Millet

Corn

Sorghum

Sweet Potato

Fired Wheat Flour

Other Cereals (Specify: )

E2 Meats and Meat Products

AverageIntake/Time

Frequency


Liang Filling According to the Actually Consumed Filling 99

Pork Meat (Muscle)

Pork Meat (Fat & Muscle )

Pork Steak

Pork Leg and Feet

Beef Meat

Mutton Meat

Chicken Meat

Duck, other Poultry

Pork and other Animal Liver

Sausages

Salted Meats

Other Sausages with Starch

Other Organs (Specify: )

E. Please estimate your average eating frequency and quantity for the following foods in the past one year


E3 Fish and Fish Products

AverageIntake/Time

Frequency


Liang Filling according to the actually consumed Filling 99

Fresh Water Fish

Sea Fish

Shrimp

Dry Shrimp

Crab

Octopus

Salted Fishes

Shell Fish

E4 Milk and Milk Products

AverageIntake/Time

Frequency



Whole Fresh Milk (Cup)

Low Fat Fresh Milk (Cup)

Whole Milk Powder (Spoon)

Low fat Milk Powder (Spoon)

Fresh Sheep Milk (Cup)

Cheese (Pieces)

Yogurt (Cup)

Ice Cream (Pieces)

Other Dairy Products (Specify: )

E5 Eggs

AverageIntake/Time

Frequency



Chicken Egg

Duck Egg

Goose Egg

Salted Chicken Egg

Salted Duck Egg

Quail Egg

Preserved Egg

30 ZHAO ET AL.

E7 Salted Vegetables

Averageintake/time

Frequency



Salted Radish

Salted Cuconber

Preserved Sichuan Pickle

Pickles

Formented Tofu

E8 Snack and Nuts

AverageIntake/Time

Frequency



Cakes

Bread

Other Sweet (Specify: )

Peanut

Walnut

Chestnut

Sunflower Seed

Other nuts (Specify: )

E9 Beer, Wine and Liquor

AverageIntake/Time

Frequency



Liquor, Low Alcohol Content (<38)

Liquor, High Alcohol Content ( >38)

Beer Cup

Wine, Champagne

Other Wine

E6 Legume and Products

AverageIntake/Time

Frequency


Bowl Liang Filling According to the Actually Consumed Filling 99

Tofu

Tofu Paste

Soy Bean Milk

Other Soy Bean Products

Fried Tofu

Dry Soy Bean

Other Dry Bean (Specify: )


10 Fungi and Mushroom

AverageIntake/Time

Frequency



Dry Mushroom

Kelp

E11 Fresh Vegetables

AverageIntake/Time

Frequency



Vegetables Total

Name of Vegetables Consumed and % of the Total Intake

Code Name of Vegetable Frequency (Times/Year) % of Total Intake Food Code

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

32 ZHAO ET AL.

E12 Fruits

AverageIntake/Time

Frequency



Water Melon

Hami Melon

Apple

Pear

Orange Juice

Grape

Peach

Dates

Melon

Strawberry

Apricot

Plum

Fresh Longan

Lichee

Pineapple

Persimmon

Hawthorn

Canned

Dried Grapes

Other Dried Fruits (Specify: )

E13 Tea and Drinks

AverageIntake/Time

Frequency


Cup Filling According to the Actually Consumed Filling 99

Black Tea

Jasmine Tea

Coffee

Coke

Other Soft Drink

E14 Sugar and Starch

AverageIntake/Time

Frequency



Sugar

Other Candy (Specify: )

Starch Noodle


E15\E16 The Following Questions be Asked per Family by Month, How Many Persons are There Eating Together in the Family _____ ?

E15 Edible Oil Food Code Intake (Jin/Month/Family ) Intake/Person/Day* (Gram/Day)

Peanut Oil

Soy Bean Oil

Grape Seed Oil

Mixed Vegetable Oil

Other Vegetable Oils (Specify: )

Pork Fat

Other Animal Oil (Specify: )

E16 Spices Food Code Intake (Jin/Month/Family ) Intake/person/day* (Gram/Day)

Salt

Soy Sauce

Vinegar

Catsup

Sesame Catsup

Monosodium Glutamate

Note. * Individual intake(g/day) = family intake (Jin\month) × 500 ÷ family members ÷ 30

E17 Food Supplementsand Medicines

AverageIntake/Time

Frequency


Dose Quantity Filling According to the Actually Consumed Filling 99

Multiple Vitamins

Vitamin A

Vitamin E

Fish Liver Oil

Vitamin C

Multiple Vit B

Vitamin B1

Vitamin B2

Vitamin B6

Vitamin B12

Folic Acid

Iron

Zinc

Calcium

Apirin

Other Supplements

34 ZHAO ET AL.

E18 Other Food Frequently Consumed

AverageIntake/Time

Frequency



Inquirer: __________ verifier: __________

Appendix 2: 24-hour Diet Recall Questionnaire

Name ID Date of Survey (1.First Day; 2 Second Day; 3.Third Day)

Code 2 Name of Dish 3 Raw Materials 4 Dood Code 5 Quantity(liang ) 6 Time of Eating 7 Place of Eating 8 Cooking Method

Note. 6 meals : (1) breakfast, (2) morning; snack, (3) lunch, (4) afternoon snack, (5) dinner, (6) night snack; 7 place of eating : (1) home , (2) office, (3) restaurant/street, (4)relative house; 8 cooking method : (1) boiling, (2) fry 3-fried, (4) steam, (5) bake, (6) raw.

Time of data collected Year Month Day Inquirer: Verifier:


Appendix 3: Study Design and Methodology

1st 24 hr food recall

1999.11Mon.Tue.Wed.

FFQ 1 (1998.9-1999.10)

2nd 24 hrfood recall

2000.1

Tue.Wed.Thurs.

3rd 24 hr food recall

2000. 3

Wed.Thurs.Fri.

4th 24 hr food recall

2000. 5

Thurs.Fri.Sat.

5th 24 hr food recall 2000.7

Fri.Sat. Sun.

6th 24 hr food recall.

2000. 9

Sat. Sun.Mon.

FFQ 2(1999.11-2000.10)

Appendix 4: Converting Table for Food Weight by Food Portion Size and Model

Food Item Food PortionSize/ Model

Weight (Raw ) Explanation

Gram Liang

Cooked Rice 1 Small Bowl 75 1.5 Diameter of Bowl = 11-12 cm

1 Large Bowl 150 3 Diameter of Bowl = 14 cm

Porridge 1 Small Bowl 30 0.6

1 Large Bowl 50 1

Rice with Water 1 Small Bowl 80 1.6 Rice /Water = 8/2

1 Large Bowl 120 2.4

Steamed Bread 1 Piece 100 2 If Home Made, Please EstimateAccordingly

Fresh Noodles 1 Large Bowl 150 3 500 g = 400 g Dry Wheat Flour

1 Small Bowl 100 2

Dry Noodles 1 Large Bowl 100 2

1 Small Bowl 75 1.5

Bread with Filling 1 Piece 50 1 3-4 Pieces = 1 Liang

Dumpling 6 Pieces 50 1 The Weight of Filling not Included

Wonton 9-10 Pieces 50 1 The Weight of Filling not Included

Fried Dough 1 Piece 50 1

Fried Cake 1 Piece 70-80 1.4-1.6


FIG. 1. Follow chart of data collection.

36 ZHAO ET AL.

Food Item Food PortionSize/ Model

Weight (Raw ) Explanation

Gram Liang

Fried Stick Rice Cake 1 Piece 50 1 Stick Rice=35 g, Red Bean =15 g

Bread with Filling 1 Piece 50 1 Wheat Flour =35 g, Red Bean = 15 g

Rice Glue Ball 3 Pieces 50 1 3 g Sugar in Each

Pancake 1 Piece 50 1

Chicken Leg 1 Piece 220 4.5 Including Bone

Chicken Wing 1 Piece 200 4 Including Bone

Sausage (Cantonese Style) 1 Piece 27 0.5

Fired Fresh Vegetables 1 Standard Dish 500 10 Weight of Raw Fresh Vegetables

Milk 1 Standard Cup 250 5

Yogurt 1 Standard Cup 250 5

Milk Powder 1 Standard Spoon 10 0.2

Chicken Egg 1 Piece 60 1.2

Duck Egg 1 Piece 70 1.4

Quail Egg 5 Pieces 50 1

Soft Tofu 1 Small Bowl 250 5

1 Large Bowl 300 6

Beer 1 Standard Cup 250 5

Peanut(with Shell) 1 Small Bowl 120 2.4

Peanut(without Shell) 1 Small Bowl 200 4

Chestnut 10 Pieces 50 1

Walnut (with Shell ) 3-4 Pieces 50 1

Apple Standard Model 210 4

Pear Standard Model 250 5

Banana Standard Model 180 3.5

Peach Standard Model 150 3

Orange (Medium Size) 1 Piece 125 2.5

Salt 1 Standard Spoon 20

( continued )


Appendix 5: Principle of Food Grouping

Food Group Food Item Included

1 Rice Rice, Stick Rice, Rice Noodle

2 Wheat Flour Wheat Flour, Fired Wheat Flour

3 Other Cereals Millet, Corn, Sorgan

4 Cereal Total = 1+ 2+3

5 Potatoes Sweet Potatoes

6 Legume Products Tofu, Tofu Paste, Soybean Milk, other Soybean Products, Fried Tofu

7 Dried Bean Dry Soy Bean, other Dry Bean

8 Legume & Products =6+7

9 Fresh Vegetable All Kind Fresh Vegetables

10 Salted Vegetables Salted Mustard, Salted Radish, Salted Cucumber, Pickles

11 Fungi Mushroom, Kelp, Agaric

12 Fresh Fruits Watermelon, Cantaloupe, Banana, Apple, Pear, Orange Juice, Grape, Peach, Dates, Melon, Strawberry, Apricot, Plum, Fresh Longan, Lichee, Pineapple, Persimmon, Hawkthorn, other Fruits

13 Nuts Peanut, Walnut, Chestnut, Sunflower Seed, etc.

14 Pork Pork Meat, Pork Steak, Pork Leg and Feet, Salted Meat, etc.

15 Beef Beef Meat

16 Mutton Mutton Meat

17 Other Meat Sausage, Liver, and other Organ

18 Meat Total =14+15+16+17

19 Poultry Chicken, Duck, Goods, other Poultry

20 Aquatics Products Fresh Water Fish, Sea Fish, Shrimp, Crab, Shell Fish, Octopus, etc.

21 Egg Chicken Egg, Duck Egg, Goods Egg, Quail Egg

22 Milk and Products Whole Fresh Milk, Low Fat Fresh Milk, Whole Milk Powder, Low Fat Milk Powder, Fresh Sheep milk, Yogurt, etc.

23 Vegetable Oil Peanut Oil, Soy Bean Oil, Grape Seed Oil, Mixed Vegetable Oil, etc.

24 Animal Fat Pork Fat, other Animal Fat

25 Cakes Cakes, Bread, other Sweet, etc.

26 Sugar and Starch Sugar, Candy, Starch

27 Soy Sauce Soy Sauce

28 Salt Salt

29 Liquor Liquor

30 Beer Beer

31 Wine Wine

32 Alcohol Total = 29+30+31

38 ZHAO ET AL.

Appendix 6: General Information of the Subjects

TABLE 1

Distribution of the Subjects by Age and Sex

Age Group(yrs)

Male Female Total

n % n % n %

25-34 30 22.9 21 15 51 18.8

35-44 29 22.1 40 28.6 69 25.5

45-54 43 32.8 51 36.4 94 34.7

55-64 29 22.1 28 20.0 57 21.0

Total 131 48.3 140 51.7 271 100.0

TABLE 2

Distribution of the Subjects by Region and Sex

Male Female Total

n % n % n %

Beijing Rural 35 26.7 46 32.9 81 29.9

Wuxi City 48 36.6 47 33.6 95 35.1

Taicang Rural 48 36.6 47 33.6 95 35.1

Total 131 48.3 140 51.7 271 100.0

Appendix 7: Name List of Working Team Members in the Survey Sites

1. Jiangsu Sanitary and Anti-epidemic StationBao-Jun YUAN (Team leader), Xiang-Ling GAO, Xiao-Qun PAN, Kai LIU

2. Wuxi Sanitary and Anti-epidemic StationXiao-Juan HUA (Team leader), Na SUN, Wei-Jie ZHOU, Yong-Cai AI, Jia XU, Ya-Mei GE, Hong-Fang SHI, Hong-Tu WANG, Ruo-Feng FENG, Lei XU

3. Taicang Sanitary and Anti-epidemic StationXiu-Ying GU (Team leader), Cai-Fen HUANG, Hong XU, Xiang-Qin YANG, Lian-He XU, Zhi-Lin ZHU, Feng PANG, Yong-Bin CAI, Zhen-Qing ZHOU, Da-Qing GU, Li-Dong WANG, Xiao-Feng QIN, Jian-Wen XIAO, Sheng-Jun QIAN

4. Daxing District Sanitary and Anti-epidemic Station of BeijingJie GAO (Team leader), Dong-Mei LI, Hai-Bo LIU, Jian-Xin SONG, Wen-Ping HOU, Shu-Hua MA, Sui-Rong GE, Qing-Ying HAN, Zhi-Hui ZHANG, Bao-Yun YUAN, Wei-Na ZHENG

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