Study Sample and Setting
We employed a purposive sampling strategy to recruit 220 adult participants aged 18 years and above from Ibadan, southwestern Nigeria. We meticulously designed the sampling to ensure a diverse and representative study sample by considering variables such as age, gender, occupation, and ethnic/tribe to capture a broad range of food consumption patterns. Our objective was to achieve proportional representation across the three major Nigerian tribes – Yoruba, Hausa-Fulani, and Igbo – while reflecting the socio-economic and age demographics of the Oyo State region, where Ibadan is situated [17]. This approach enhanced our ability to encompass a wide range of perspectives and characteristics within the target population. Participants gave informed consent prior to their enrollment in the study. The study was approved by the Nigerian National Health Research Ethics Committee and the Institutional Review Board (IRB) of the University of Maryland.
Study Sample Characteristics.
We gathered data on age in years, years of education completed (< 11 years of school, 12 years of school, post-secondary school and university), occupation (Unemployed, Self-Employed, Skilled Manual, Professional/Executive), Tribe (Yoruba, Igbo, Hausa/Fulani, Other), and dietary habits such as how often home-cooked meals were consumed and the most and least common locations for food consumption.
Dietary Intake Data
We used a validated semi-quantitative Food Frequency Questionnaire (FFQ) to measure participants' food intake at baseline and every 6 months for a total of four assessments between November 2018 and October 2020. Briefly, the FFQ is complemented with a Food Picture Book (FPB) featuring typical Nigerian foods and their corresponding standardized portion sizes. Further details on the Nigerian FFQ and FPB can be found elsewhere [14]. The FFQ includes about 200 food items where frequencies of their consumption were reported on a monthly, weekly, and daily basis ranging from “Never or less than once per month” to “6 or more times per day.” Participants were asked to choose the option that most accurately represents their typical or average consumption of the listed food item during the past year. Each reported food items in number of portions were then converted to grams per day as follows: frequency of intake × conversion factor for daily intake × total number of portions × portion weight. Conversion factors were derived from intake frequency, adjusted to represent daily intake. For instance, if a certain food was reported to be consumed 2–4 times a week, we multiplied the frequency by 3/7 to convert it to daily intake. All the FFQs evaluations were conducted by the same trained personnel in participants' homes. No data were collected during holidays, festivals, or weekends. All data were doubly entered into the Research Electronic Data Capture (REDCap) database [18, 19].
The Global Diet Quality Score (GDQS)
We employed the GDQS approach to evaluate the healthiness of food consumption in our study population. GDQS analyze diet data at the food group level to gain insights into the types of foods consumed at the population level. After we calculated the daily food intake in grams for each food reported at the FFQs, we categorized them into their respective GDQS food groups as shown in Table 1. Mixed dishes reported in the FFQ, such as soups like egusi and bitter leaf, were disaggregated into individual foods using standard recipes [20]. For simple dishes with up to three ingredients, such as white rice, breakfast cereal, and 'swallows,' we estimated the weights of each ingredient in the prepared foods. For instance, the weight of one portion of prepared oats with water/milk was 456 grams; this was multiplied by one-fifth to estimate the weight of the oats alone and the value obtained was used to calculate the white grain intake in this food.
Table 1. GDQS and GDQS Sub-Metrics (GDQS +, GDQS-) Food Groups and Scoring and all the applicable food items collected from the FFQ
Included in the GDQS
|
Included in the GDQS+
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Food group
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Scoring ranges (g/day)
|
Respective point values
|
Applicable food items from the FFQ
|
Citrus fruits
|
<24, 24–69, >69
|
0,1,2
|
Orange; Tangerine; Lemon; Lime
|
Deep orange fruits
|
<25, 28–123, >123
|
0,1,2
|
Mango; Pawpaw; Apricot
|
Other fruits
|
<27, 27–107, >107
|
0,1,2
|
Banana; Apple; Guava; Plum; Peach; African Pear (IG: Ube); Avocado; Passion fruit; Tamarind (HA: Tsamiya); African cherry (YR: Agbalumo); Pineapple; Grapes; Watermelon; Jackfruit (Bread fruit; Sweet melon; Berries; Baobab (HA: Kuka fruit); Fruit salad; Plantain
|
Dark green leafy vegetables
|
<13, 13–37, >37
|
0,2,4
|
Waterleaf, (Vegetable soup, Afang, Utazi, Edi ka ikong, Uziza); Pumpkin leaf (Egusi soups); Bitter leaf (Bitter leaf soup, banga soup); Amaranthus leaves (HA: Ayoyo, YR: Ewedu))
|
Cruciferous vegetables
|
<13, 13–36, >36
|
0, 0.25, 0.5
|
Coleslaw
|
Deep orange vegetables
|
<9, 9–45, >45
|
0, 0.25, 0.5
|
Carrot; Pumpkin, yellow and orange squash
|
Other vegetables
|
<23, 23–114, >114
|
0, 0.25, 0.5
|
Cucumber; Garden egg; Vegetable salad; Okra
|
Legumes
|
<9, 9–42, >42
|
0,2,4
|
Bean alone; Beans porridge; Bean cake (YR: Akara); Corn with beans (YR: Adalu); Bean pudding (YR: Moinmoin); Soya drink; Bean Soup (Gbegiri)
|
Deep orange tubers
|
<12, 12–63, >63
|
0, 0.25, 0.5
|
Sweet potato – Boiled/Fried; Potato porridge (sweet potato)
|
Nuts and seeds
|
<7, 7–13, >13
|
0,2,4
|
Groundnut (Cooked/Roasted); Cashew nut; Tiger nut (YR: Ofio, HA: Aya, IB: Akiausa); Walnut (YR: Asala); Kwuli-kwuli; Peanut butter
|
Whole grains
|
<8, 8–13, >13
|
0,1,2
|
Oats (i.e., Quaker Oats); High fiber cereals i.e., Bran; Brown rice; Corn (Roasted/Boiled); Corn and beans (YR: Adalu); Tuwon masara (swallows); Pap from corn (YR: Akamu, Ogi); Millet meal (Pap); Tuwon dawa (guinea corn) (swallows); Tuwon gero (millet); Popcorn
|
Liquid oils
|
<2, 2–7.5, >7.5
|
0,1,2
|
Palm oil
|
Fish and shellfish
|
<14, 14–71, >71
|
0,1,2
|
Snail; Shrimp and Prawns; Fish Sea-Water (boiled/fried); Fish Fresh-Water/River (boiled/fried); Dried/Smoked fish; Sardines
|
Poultry& game meat
|
<16, 16–44, >44
|
0,1,2
|
Chicken (with/without skin) - Broiled, fried or grilled; Turkey; Guinea fowl
|
Low-fat dairy
|
<33, 33–132, >132
|
0,1,2
|
Fresh milk; Chocolate drink (Milo, Bournvita etc.) with milk; Custard
|
Eggs
|
<6, 6–32, >32
|
0,1,2
|
Chicken egg (Boiled/ Fried).
|
|
Included in the GDQS-
|
High-fat dairy
|
<35, 35–142, >140–734, >734
|
0, 1, 2, 0
|
Evaporated liquid milk; Powdered milk; Yoghurt (plain/sweet); Cream cheese.
|
Red meat
|
<9, 9–46, >46
|
0,1,0
|
Beef (boiled/fried); Goat Meat (boiled/fried); Pork (Pig meat)(boiled/fried); Lamb/Mutton)(boiled/fried); liver (boiled/fried); Offal/Tripe (YR: orisirisi)
|
Processed meat
|
<9, 9–30, >30
|
2,1,0
|
Bushmeat; Bacon; Processed cow skin (YR: Ponmo, Bokoto, Cow-Leg); Canned meats (Bully beef/Corned beef); Meat minced; Suya
|
Refined grains and baked goods
|
<7, 7–33, >33
|
2,1,0
|
Spaghetti; Noodles (e.g. Indomie); Macaroni; Rice (White, Jollof, Fried, Ofada, Coconut); Ground rice (Rice flour) swallow; Wheat flour swallow; Tuwon shinkafa; Bread (sliced; flat); Meat Pie (Meat samosa), e.g. Gala; Bread rolls; Pancakes; Vegetable samosa; Doughnut, Fried dough, Buns, Puffpuff; Semolina; Breakfast cereal e.g. Cornflakes, Rice Krispies.
|
Sweets and ice cream
|
<13, 13–37, >37
|
2,1,0
|
Ice cream; Cake, tarts, scones, muffins; Chocolate bar; Sugar added to foods (include in tea & coffee); Honey; Jam; Marmalade; Sugarcane; Raisins (Cake fruit); Dates (HA: Dabbino)
|
Sugar-sweetened beverages
|
<57, 57–180, >180
|
2,1,0
|
Soda - regular (Coke, Fanta, etc.); Soda – Diet; Chocolate drink (Milo, Bournvita etc.) without milk
|
Juice
|
<36, 36–144, >144
|
2,1,0
|
Orange or other fruit juices (sweetened/ unsweetened) Fruit squash, concentrate – mixed with water (sweetened/with artificial sweetener)
|
White roots and tubers
|
<27, 27–107, >107
|
2,1,0
|
Irish potato (boiled/fired); Boiled Cassava; Potato porridge (Irish potato); Yam porridge; Yam (boiled/fired); Traditional Pounded Yam; Pounded Yam from Flour; Garri; Eba (Swallows); Cassava flour swallow (YR: lafun); Cocoyam (Boiled/Chips); Amala (swallows); Fufu or Akpu (swallows)
|
Purchased deep fried foods
|
<9, 9–45, >45
|
2,1,0
|
Potato or Corn chips or crisps; Cocoyam chips; Doughnut, Fried dough, Buns, Puffpuff; Plantain chips (YR: Igbekere/Ipekere); Fried Sliced Plantain (YR: Dodo); Fried Yam.
|
HA, Hausa/ Fulani; IB, Igbo; YR, Yoruba. GDQS; Global Diet Quality Score; GDQS +, healthy food groups; GDQS-, unhealthy food groups
|
Concisely, the GDQS comprises of 25 food groups overall, based on their impact on enhancing or reducing the overall quality of individual diets (scores ranging from 0 to 49 are assigned). Each ingredient/food item was assigned to an appropriate GDQS food group. Foods in the category of purchased deep-fried items were "double-counted," meaning they were included in both the deep-fried foods group and another group based on the characteristics of the foods. For example, fried yam (Dundu) was counted in both deep-fried foods and in the white roots and tubers groups. We computed the overall daily intake of each food group within the GDQS in grams. The 25 food groups were further categorized into GDQS+ (healthy foods) which consists of 16 food groups that contribute positively to the overall diet quality score (dark-green leafy vegetables, deep-orange vegetables, deep-orange fruits, deep-orange tubers, cruciferous vegetables, other vegetables, citrus fruits, other fruits, fish and shellfish, poultry and game meat, legumes, nuts and seeds, low-fat dairy, eggs, whole grains, and liquid oils), and GDQS- (unhealthy foods) comprising 7 food groups that have a negative impact on the overall diet quality score (white roots and tubers, processed meat, refined grains and baked goods, sugar-sweetened beverages, juice, sweets and ice creams, and purchased deep-fried foods). Additionally, 2 food groups (red meat and high-fat dairy) are classified as "unhealthy in excessive amounts," where optimal intake improves while excessive intake reduces the overall diet quality score (red meat and high-fat dairy).
Values corresponding to each GDQS food group were assigned according to daily intakes as shown in Table 1. Specifically, each food in GDQS + were assigned points ranging from 0 to 4 for each level of intake, while foods in GDQS- were assigned 2, 1, and 0 points. This scoring system ensures that higher intakes of healthy foods and lower intakes of unhealthy foods resulted in higher GDQS scores. Red meat and high-fat dairy are assigned a score of 0 when they are consumed in either low or excessive amounts. Red meat is assigned a score of 1 when consumed in moderate amounts. High-fat dairy received scores of 0, 1, 2, and 0 across four levels of intake where 0 is assigned for low amounts intake and 0 for 'unhealthy in excessive amounts.' The total possible score for the overall GDQS ranged from 0 to 49 while that of the GDQS + ranged from 0 to 32, and that of GDQS- ranged from 0 to 17. The overall GDQS and its sub-metrics (GDQS + and GDQS-) offer insights into the balance between healthy and unhealthy food group consumption, with GDQS helping to identify individuals at low risk (GDQS ≥ 23), moderate risk (23 > GDQS ≥ 15) and high risk (GDQS < 15) of poor diet quality. For additional information on the GDQS, please refer to these references [12, 21].
Statistical Analysis
We used STATA 18.0 (STATA Corp LP) for data analyses and set statistical significance at p-value < 0.05. Dietary intakes were assessed and analyzed based on the FFQ administered at baseline using cross-sectional approach. When comparing the seasonality of GDQS and its sub-scores (GDQS + and GDQS-), we used all four FFQs. Categorical variables are presented as frequencies and percentages, while continuous variables are presented as means and standard deviations. We reviewed outliers against the original questionnaire, and these were resolved where possible, and we excluded unresolved cases. Where participants gave data with inadequate time intervals between the questionnaires to ensure that we captured different seasons or years, the observations were excluded, resulting in a final sample of 205 out of the initial 220. This accounted for less than 7% (15/220) of the total study sample. We employed chi-square analysis for categorical variables and t-tests for continuous variables. Based on the Shapiro–Wilk test, the GDQS data was not normally distributed (p < 0.001). Consequently, we employed the Kruskal-Wallis equality of population rank test to examine differences in GDQS and its sub-metrics (GDQS+, GDQS-) across study characteristics and dietary habits. We utilized all four FFQs to assess differences in GDQS and its sub-metrics (GDQS+, GDQS-) between men and women separately for the rainy and dry seasons, employing the Wilcoxon signed-rank test. In the final analysis, we employed Generalized Linear Model (GLM) utilizing the family (gaussian) option with the identity link function to investigate the relationship between GDQS and its sub-metrics as primary outcomes, alongside demographic characteristics, and dietary habits as independent variables in the multivariable models.