Study design and Participants
We validated the FFQ against the average of two 24-HRs. The FFQ was obtained after the second 24-HR. There was a 15 days interval between the first and the second 24-HR. We have used an interactive, multiple pass 24-HR method adapted and validated for use in developing countries (15). We conducted the study among randomly selected 120 Ethiopian adults aged 20 to 65 in Butajira Health and Demographic Surveillance Site (HDSS), from March to April 2019. We employed simple random sampling to identify study participants. Households with adults aged 20-65 were filtered out from the HDSS data registry to form a sampling frame. From this frame, we randomly selected 120 households with adults aged 20-65. We visited all randomly selected households with adults aged 20- 65 with support from the health extension workers, local guides and study supervisors. To be included in the study, the participants had to complete an FFQ and two 24-HRs and had fewer than 10% of their FFQ items missing. After explanation about the purpose, and related procedures of the study verbal informed consent was obtained from the study participants.
Development of the FFQ
Figure 1 shows the process of FFQ development.We followed five steps to develop the FFQ: choosing appropriate foods, prioritization, and categorization of food items, assembling a list of selected foods, frequency and portion size, and expert review and pre-testing. First, we obtained information on dietary intakes from an unpublished cross-sectional dietary survey of women (n 384) living in rural and urban households of Butajira Health and Demographic Surveillance Site (HDSS), in Southern Nations and Nationalities and Peoples Regional States (SNNPR) from 2018-2019. Information on dietary intake was collected using a single multiple-pass 24-hr technique with women in their own homes. The survey was part of a mother-child cohort study (BUNMAP) in Butajera, Southern Ethiopia which is on “The economic, psychological, safety and quality aspects of food and nutrition and the effects on pregnancy outcomes, child growth and development (16).
We undertook Market and mini-market visits on non-consecutive days to identify common brand names and foods that could be relevant and were added accordingly. Besides, we conducted a focus group discussion in Butajira district two weeks before the interview. It was organized by the principal investigator and field supervisors. We interviewed a group of 6 women about foods consumed in the area. We recruited those women by the help of health extension workers, local guides and study supervisors. The women came from urban and rural areas of Butajira. They were selected purposively. The aim of the focus group discussion was to probe and discuss the food items in that specific area in order to have comprehensive list of food types. We undertook the discussion to identify food items that are typically consumed, including ingredients used and methods of preparation. Second, we combined similar foods and beverages into a single group of food items. Third, we clustered the related food items together. To facilitate dietary reporting, food groupings should ﬁt within respondents’ conceptual framework. We clustered related items together, such as traditional food groups. For closely related foods, we placed more speciﬁc items before general items. We have used results of our focus group discussion to help construct lists for culturally speciﬁc questionnaires or to provide information about which foods should be grouped together.
Forth, we evaluated the frequency of intake based on the usual intake over 1 month before data collection. We included seven frequency categories ranging from daily to never/one less than per month. Three women were involved in the cooking process and portion size estimation. We assigned a portion size for each food item. We employed a pre-specified portion size estimation method for the estimation of portion size in FFQ using local house-hold units such as bowl, plate, spoons of different sizes (tablespoon, teaspoon), coffee-cups, tea-cups, water glasses, as well as using photographs. The data for preparing a pre-specified portion size is based on data obtained from food lists created on step one, focus group discussion and local markets and shops visits. To determine the weight of the food items used we made commonly consumed dishes in the Ethiopian public Health Institute laboratory. We did the measurement with an Electronic Seca scale and the average of the 3 measurements was taken. We gave codes for different prepared portions. To help standardize participants understanding the interviewer prepared photographs for each measurement done and showed them to the participants.
Fifth, experts reviewed the newly developed FFQ (nutritionist from Addis Ababa University) to confirm its content validity. We have discussed the food list extensively to ensure all relevant food items were included. Pre-test was conducted in a group of 10 randomly selected adult women who are comparable to the study participants in one non-sampled kebele. Some minor changes were made based on the finding of the pre-test.
The developed FFQ consisted of 89 food and drink items. The food groups include cereals, bread and potatoes, Legumes and pulses, Roots and tubers, vegetables, fruits, egg, milk and dairy, fish and fish-products, meat and poultry, fat and oil, sweets, drinks, and fast foods and pastry.
24-Hour Dietary Recall
We have used an interactive, multiple pass 24-HR method adapted and validated for use in developing countries (15). We conducted the two 24-HRs on non-consecutive days. We interviewed on weekdays and weekends to capture variance in the intakes across various days of the week. Before data collection, we gave a rigorous training and conducted a pre-test. We recruited three (3) interviewers who had a previous experience in dietary data collection and fluent in local language. Each interview involved a stepwise series of questions.
First, we asked the participants to report everything that they consumed the previous day, including the night. The opening question was; “After you got up this morning/yesterday morning, when was the first time that you had something to eat or drink?” followed by the questions “What did you eat or drink at that time?” and “Did you eat or drink anything else at that time?” The same three questions were repeatedly been asked until the participants recall all the food and drink items consumed over the specified period. The first pass ended with the questions “Can you remember any other times you had something to eat or drink to?” In the second pass, participants were asked to provide additional detailed information about each item of food and drinks consumed. This includes the name of the food item, where they ate it, brand names, cooking methods, amounts served, and the amount consumed. For homemade dishes, participants were asked for the recipes and ingredients.
On the third pass we used common household utensils such as bowl, plate, spoons of different sizes (tablespoon, teaspoon), coffee cups, teacups, water glasses to improve the memory of the respondents and to assist in completing the recall. To estimate portion size, each participant was asked to put amount of food that is equivalent to the actually eaten on food weighing scale. Then the data collectors measured the weight of the food consumed and recorded it. The final pass reviewed all previously recalled information to confirm the accuracy of the record. During the final pass, the data collectors asked the participants to prompt for information about foods and drinks not mentioned that were considered to be easy to forget, such as snacks, fruits, water, and juices (17).
Food Frequency Questionnaire
We evaluated the frequency of intake based on the usual intake over the previous month. We included nine (9) frequency categories ranging from daily to never/one less than per month each food item was assigned a pre-specified portion size.
Calculation of Daily Food and nutrient Intake
We used the Ethiopian food composition table to derive nutrient and energy estimates from the dietary data (18). The names of foods and drinks, their description, cooking methods, and amounts from both 24-HR and FFQ, were coded and entered into NutriSurvey2007. The FFQ consisted of 89 food and drink items. We organized the food lists were into 14 food groups on the basis of prior information. We calculated food estimates from FFQ using the product sum method. We converted the average frequency of food intake per week and month of the FFQ to a daily intake value (e.g., frequency of 2–3 times per month = 2.5/30.5 times per day. Once the frequency of consumption per day was calculated, we computed the daily food intake using the product sum method. Daily food intake =∑ (reported consumption frequency of the food item, converted to times per day) *(portion size consumed of that food).
Statistical test of validity
We checked both the FFQ and 24-HR data for completeness and potential errors. We then entered the data on socio-demographic characteristics using Epi-Data version 3.1 and exported to STATA version 15 for further processing and analysis. Out of 120 study participants, 118 (98.3%) of participants completed 1st 24-HR, 116 (98.6%) completed 2nd 24-HR, 116 (98.6%) completed both 24-HRs and 115 (95.8%) participants completed both the 24-HRs and the FFQ.
We checked the normality of the average intake of nutrient and food groups using the Shapiro-Wilk normality test and visualized using Q-Q plots. We used parametric tests for normally distributed variables, while non-parametric tests were used for most of the variables as the distributions signiﬁcantly deviated from normality. Those which fulfilled the assumption of normality were described using mean with standard deviation (SD) and those which do not use median with inter-quartile range (IQR).
We evaluated the performance of the FFQ against two 24-HRs using several statistical tests. First, to compare median daily food intakes obtained from the averages of the two 24-HRs and the FFQ, we used the Wilcoxon signed-rank test. To evaluate the agreement between the two methods, we compared the mean daily food intakes obtained from the averages of the two 24-HRs and the FFQ using paired t-test. Second, to measure the strength and direction of the correlation between the two methods, we computed the crude Pearson correlation for normally distributed variables, whereas crude Spearman’s rho for those not normally distributed. The cut-off points used for correlation coefficient are as follows; <0.20 as low correlation (poor outcome), 0.20 - 0.49 as moderate correlation (acceptable outcome), and ≥0.50 as high correlation (good outcome) (13).
We have calculated the de-attenuated correlations to remove the within-person variability found in the 24-HRs using the following formula:
rt = ro√1+r/n
rt is the corrected correlation between energy/nutrient/food group derived from the FFQ and 24-HRs, ro is the observed correlation, r is the ratio of estimated within-person and between- person variation in energy/nutrient/food group intake derived from the 24-HRs, and n is the number of replicated recalls (n = 2) (11).
Third, for both the test and reference methods subjects were divided into categories relating to the distribution of dietary intake; quartiles of intake. A comparison of the subjects’ categories showed whether subjects are classified in the same or different categories by the two methods. The result permitted an assessment of the proportion of subjects who are classified correctly. We used a weighted kappa statistic to account for both the correctly classified percentage and the expected participant proportion classified by chance. The cut-off points used for weighted kappa statistics are as follows; <0.20 as low kappa (poor outcome), 0.20 - 0.60 as moderate kappa (acceptable outcome), and ≥0.50 as high kappa (good outcome) (13). At last, we used a Bland and Altman plot for assessing limits of agreement between the two methods. The Bland-Altman method is preferable to compare two measurements each of which produced some error in their measures (19).