Validation of a computer-based analysis tool for real-time dietary assessment within a Ghanaian region

NFS Journal 16 (2019) 15–25

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Original article

Validation of a computer-based analysis tool for real-time dietary assessment within a Ghanaian region ⁎

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Julian Philipp Walda, , Emmanuel Asarea,b, , Emmanuel Kweku Nakuab, Donatus Nohra, Christine Lamberta, Simon Riedela, Ute Golaa, Hans Konrad Biesalskia a b

Institute of Biological Chemistry and Nutrition, University of Hohenheim, Stuttgart, Germany School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

A R T I C LE I N FO

A B S T R A C T

Keywords: Food security Malnutrition Micronutrients Hidden hunger Sub-Saharan Africa CIMI

The availability of and accessibility to adequate diets that meet individual nutritional needs and personal food preferences are key elements to guarantee food security. However, suitable solutions for quick assessments of dietary energy and nutrient intakes, in particular at the individual level, are still not available. Therefore, this study illustrates the validation and application of the recently developed calculator of inadequate micronutrient intake (CIMI) approach within a Ghanaian region. The basic structure of CIMI is formed by food frequency questionnaire findings and food composition tables that are used to identify region-specific food groups. CIMI validation includes the correlation and plotting of results obtained from the analysis of 24 h-Recalls with CIMI and a standard nutrition software (NutriSurvey®). Pearson correlation coefficients and Bland-Altman plots indicate method comparability and thus, the validity of CIMI. In comparison with recommended nutrient intakes developed by the World Health Organization, CIMI shows that dietary needs are largely met in the study region in adult men, women and children under-five. However, inadequate dietary intakes were identified for calcium, riboflavin and folic acid and in some subgroups for iron, zinc, vitamin A, vitamin D and niacin. Due to the userfriendly data entry system and the real-time survey analysis, CIMI will assure fast and valid dietary assessments and represents a first step towards the collection of large-scale datasets on individual dietary intakes. This approach will fill data gaps and serve as a profound basis for stakeholders to recommend appropriate policy to sustainably address nutrient deficiencies through coordinated agriculture for diversified food supplies.

1. Introduction In 1996, the World Food Summit defined food security as “a condition that exists when all people, at all times, have physical and economic access to sufficient safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” [1]. Based on this concept, the availability to and accessibility of adequate diets that meet individual dietary requirements with respect to personal preferences represent key elements to guarantee food security. However, it is estimated that nearly a billion people are suffering from insufficient dietary energy availability [2] and more than twice that number are experiencing inadequate micronutrient supplies [3]. While severe micronutrient deficiencies may lead to clinical symptoms (e.g. night blindness for vitamin A deficiency), a much larger proportion of the population is affected by less obvious effects [4], also known as “hidden hunger”. The effects of hidden hunger on health and survival can be profound, especially within the first 1000 days of life, from

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conception to the age of two, resulting in serious physical and cognitive consequences [5]. Within this context, vitamin A, iron and zinc represent key micronutrients due to their extensively studied properties and impacts on physiological functions. However, there are other less investigated micronutrients (e.g. vitamin D, folic acid and cobalamin) which might prove to be equally crucial [6]. The nutritional status of a population serves as one of the useful health status indicators, thus, knowledge about the dietary conditions of different sub-population groups is essential for any effective nutrition program to counteract dietary gaps and achieve specific nutrition goals. This is only possible through timely nutritional assessments. However, the majority of countries still rely on national food balance sheets [7] to assess and monitor food consumption, even though these compilations do not provide information on individual food accessibility and are not conducted on a regular basis [8]. In addition, despite a multitude of other available survey instruments [9–12], data gaps and profound evidence on micronutrient intakes persist and therefore, more updated,

Corresponding authors at: University of Hohenheim, Garbenstr. 30, 70599 Stuttgart, Germany. E-mail addresses: [email protected] (J.P. Wald), [email protected] (E. Asare).

https://doi.org/10.1016/j.nfs.2019.06.002 Received 15 April 2019; Received in revised form 24 June 2019 Available online 25 June 2019 2352-3646/ © 2019 The Authors. Published by Elsevier GmbH on behalf of Society of Nutrition and Food Science e.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Table 1 Summary of defined food groups and local portion size descriptions (calculated as aspinach, bantelope, cTilapia, dCatfish; food items in italic are local names). Food group

Contributing food item

Portion size (FFQ)

1 2

Cassava Other roots and tubers

3 4 5 6

Plantain Rice Maize Other cereals and cereal-based products

7 8

Legumes Nuts

9

Vitamin A-rich vegetables

10 11

Dark green leafy vegetables Other vegetables

12 13

Vitamin A-rich fruits Other fruits

14 15 16

Meat Organ meat Fish

17 18 19 20

Egg Diary β-Carotene-rich oil Retinol-rich oil

21 22

Other oil Sugar and candy

23

Caloric Beverages

Cassava Yam Cocoyam, Potato Plantain Rice (White) Maize (White) Millet, Sorghum, Wheat Noodles, Macaroni, Oats, Pasta, Wheat flour Bambara, Cowpea, Pigeon pea, Red bean, Soybean Melon seeds (Agushi), Groundnut, Tiger nut Coconut flesh Carrot Sweet potato Amaranth leaves, Chorchorus leaves, Bokobokoa leaves, Cassava leaves, Cocoyam leaves (Kontomire) Spring onions Turkey berry (Kwawu nsusua), Okra, Pepper Cabbage, Cauliflower, Cucumber, Eggplant, Garlic, Garden egg, Lettuce, Onions, Tomato Mango, Palm nut fruit, Papaya, Watermelon Banana Grapes, Strawberry Apple, Avocado, Oranges, Pineapple, Sweet apple Beef, Bush meatb, Cattle skin (Wele), Gizzard, Goat, Intestine, Pork, Poultry, Sheep Liver Shrimps Leanc and fat fishd Crabs Egg (Poultry) Cocoa and milk powder Red palm oil Frytol (Retinol-fortified refined vegetable oil) Margarine Groundnut oil, Olive oil, Palm kernel oil, Soybean oil, Sunflower oil Chocolate Sugar (white) Toffee Malt and soft drinks Fruit/Vegetable juice, Coffee and tea bag

Palm size Arm length Palm size Finger Cup Cup Cup Package Cup Cup Piece Finger Palm size Bundle Bundle Handful Piece Piece Finger Handful Piece Pound Pound Cup Palm size Piece Piece Package Bottle (300 mL) Bottle (300 mL) Package Bottle (300 mL) Bar Cup Piece Bottle (300 mL) Package

intakes remain scarce [26,27]. Therefore, it can be assumed, that micronutrient deficiencies in specific areas of Ghana are highly prevalent and persistent. The aim of this study comprised the validation and use of the Calculator of Inadequate Micronutrient Intake (CIMI) approach to a Ghanaian region. This was realized through a region-specific adaptation of a recently developed dietary assessment tool in line with local food consumption patterns [28]. CIMI represents a user-friendly computer-based technique to determine dietary energy and nutrient intakes on individual-level. The customized version of CIMI assesses a total of 20 nutrients including macronutrients, minerals, fat- and watersoluble vitamins with a special focus on iron, zinc and vitamin A. Furthermore, its food group-based interface, the real-time dietary assessment as well as the ability to collect data and work offline makes the CIMI concept unique.

extensive and reliable data is urgently needed [4]. For that reason, the development of valid and standardized core technologies is crucial for an effective tracking and evaluation of the nutritional situation and will play an important role in designing harmonized prevention and intervention guidelines to counter diet-related deficiencies [13,14]. Among the approaches for a more detailed sub-population nutrition assessment, Food Frequency Questionnaires (FFQs) and 24 h-Recalls provide comprehensive information about dietary patterns at individual-level. They give an estimate of the actual food intake of an individual as recalled from memory. These procedures are usually time-consuming and require regional adaptations to cultural contexts as well as extensively well-trained fieldworkers to conduct surveys and subsequently calculate dietary intake or nutrient deficiencies [15–17]. In addition, there is a huge time frame between interviews and the subsequent data analysis to determine gaps in nutrient uptake. In today's age of digitalization, digital approaches could represent powerful tools to accurately assess dietary intakes frequently on a large-scale. Web-based techniques have already been developed [18–21], however, methodological innovation is still vital to simplify data collection and increase data quality [22]. According to the Global Hunger Index, Ghana has made a remarkable and steady progress in the past 25 years in improving food security across the country [23]. However, malnutrition remains a concern. Whereas 19% of children under-five are stunted, 5% are suffering from wasting. While two thirds of children between 6 and 59 months show anemic symptoms with different levels of severity, over 40% of Ghanaian women of reproductive age are affected by anemia [24]. In addition, the prevalence of anemia was significantly higher in iron and vitamin A deficient women, compared to women with adequate levels of both micronutrients [25]. However, representative data on vitamin A

2. Methodology 2.1. Study design The cross-sectional study was conducted from January to February 2016 in one rural (Ahafo Ano) and two urban communities (Kumasi and Obuasi) within the Ashanti Region of Ghana. Using systematic random selection, 42 households were randomly sampled from a previously collated list of all households in each community, making a total of 126 households. Ethical clearance was obtained from the Committee on Human Research Publications and Ethics at the School of Medical Sciences of the Kwame Nkrumah University of Science and Technology in Kumasi, Ghana. The survey questionnaire comprised data collection of food consumption on household- and individual-level, demographic 16

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Fig. 1. Schematic visualization of the CIMI program development process illustrating its construction, validation and features.

stews. For liquids, 500 g of packaged sachet water, a common source of drinking water or standardized beverage bottles were used to estimate quantities taken. In capturing estimations of the amount in grams of each ingredient in the food, every meal was subdivided into three components: main meal, accompaniment and additive. For instance, fufu with groundnut soup and fish had fufu as the main meal, groundnut soup as the accompaniment and fish as the additive. Fufu is a pounded mixture of plantain and cassava. The number of fist sized balls of fufu was recorded. To standardize the methodology, the ingredients were later converted into grams using a standard recipe [30] by multiplying the number of balls eaten with the average mass of previously weighed fist size balls of fufu considering the containing proportions of plantain and cassava. This is because respondents were unable to provide this information if they did not cook by themselves. The same procedure was followed for the two other components of the meal. For soups e.g., the same methodology was used. Standard recipes were used to determine the proportions of each ingredient within the soup. The average weight of a ladle of soup in grams was determined and the corresponding amount in grams per ladle of each ingredient was later calculated. Recipes and previously weighed portion sizes of numerous local food preparations were essential in assessing the mass of each ingredient consumed.

characteristics including physical assessments as well as socio-economic aspects. 2.1.1. Food frequency questionnaire The FFQ retrospectively assessed the frequency of consumption of food from a list of foods over a specific period [29]. In order to provide a more valid, reliable estimate of the quantity of listed raw food items consumed by the whole household per month, the questionnaire was administered to the household member directly responsible for purchasing and preparing the household food. Portion sizes were developed after a market pilot survey to assess common local portions of food items (Table 1). Clearly defined portions sizes for each of the listed 107 food items were provided to facilitate estimation of raw food quantities by the respondent. For packaged products, the commonest package sizes were used as portion size. The cup portion size for cereals, legumes, seeds and nuts was a standard local cup used in local markets (approximately 500 mL in volume). With meat, if respondents were unable to estimate their consumption in pounds, 1 pound was approximated to the weight of 1 sachet water (a 500 g common nationwide drinking water source) and 1 full local chicken was approximated to 4 pounds. For raw food items, local samples were purchased and the average weight in grams per portion size was measured with a digital weighing scale. The aim was to assess the total quantity in grams for each raw food item consumed per month at the household level.

2.1.3. Data collection and management Field surveys were conducted in a hardcopy format and subsequently transferred into digital versions. Statistical analysis was performed using STATA/SE 12.0 and SPSS version 22. All CIMI calculations regarding dietary energy and nutrient intakes were performed with a pre-version of CIMI using Microsoft Excel macros. For method comparison, Pearson correlations as well as Bland-Altman plots were created. Datasets were checked for normal distribution using Kolmogorow-Smirnow and Shapiro-Wilk tests.

2.1.2. 24 h-Recall Individual dietary intakes were assessed using the 24 h-Recall method. A structured questionnaire was used to obtain data on all foods consumed by each participant whether eaten at home, work, school or elsewhere, guided by specific probes. In each household, this data was obtained from three household members; the household head aged 19–65 years (the main male provider), a female aged 15–50 years and a child under-five. The questionnaire for the child under-five was administered to its primary caregiver. If more than one female within age of interest or child under-five was observed in a household, a simple random selection was conducted to choose one to interview. Individuals were asked to recall all foods and beverages consumed during the day preceding the interview [15]. Participants were requested to give an account of ingredients and quantities of all food items consumed from morning to evening or till they went to bed. Seven main portion size measures were used to describe quantity of food taken. Fist size was for banku, fufu, kenkey and other meals in the shape of a ball; palm size for fish; finger for plantain, bananas, carrots, etc.; matchbox size for meat; teaspoon for sugar, chocolate powder and other powders; soup ladle for rice, porridge, and soups; and stew ladle for

2.2. CIMI construction, validation and features The development of CIMI consists of two major parts (Fig. 1) and was previously described for an Indonesian setting using socioeconomic survey data [28]. To be able to refer on direct food intake rather than using non-specific data from socioeconomic surveys, dietary intake assessment questionnaires were used to generate data. Based on the FFQ survey findings, region-specific food groups were firstly identified, forming the basic structure of CIMI. For each identified food group, average nutrient profiles were calculated. In the second phase, the program was validated with the 24 h-Recalls by correlating and plotting 17

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Table 2 Classification of iron and zinc bioavailability (as seen in and adapted from [28]). Nutrient

Bioavailability

Classification

Iron

15%

If > 5% of total energy is accounted for by protein from meat, fish and other seafood Calcium intake is < 1 g If 3–5% of total energy is accounted for by protein from meat, fish and other seafood Calcium intake is < 1 g If > 50% of total energy intake is accounted from roots and tubers, cereals, legumes and nuts and 2–3% of total energy is accounted for by protein from meat, fish and other seafood If ≤50% of total energy intake is accounted from roots and tubers, cereals, legumes and nuts and 1–2% of total energy is accounted for by protein from meat, fish and other seafood If none of the described conditions is met If ≤50% of total energy intake is accounted from roots and tubers, cereals, legumes and nuts and > 5% of total energy is accounted for by protein from animal sources If > 50% of total energy intake is accounted from roots and tubers, cereals, legumes and nuts and < 2% of total energy is accounted for by protein from animal sources If none of the described conditions is met

12% 10%

Zinc

5% High Low Moderate

carbohydrates, fibre, calcium, iron, magnesium, zinc, vitamin A, retinol, β-carotene, vitamin D, vitamin E, thiamin, riboflavin, niacin, pyridoxine, folic acid, cobalamin and ascorbic acid.

CIMI results based on the preceding food groupings with results obtained from a standard nutrition software [31]. A strong correlations and low mean differences between CIMI and NutriSurvey® findings indicated comparability of the two dietary assessment procedures. Additionally, based on the region-specific food groupings, an algorithm for the respective setting was developed to assess and classify the bioavailability of iron and zinc for regional dietary patterns (Table 2). Finally, results were compared to recommended nutrient intake (RNI) requirements [32].

2.2.2. CIMI-validation using NutriSurvey® By using average values for the nutrient profiles of respective food groups, approximations to the real individual values must be expected. For that reason, the effect of food grouping on the accuracy of the procedure was assessed by correlating (Fig. 2) and plotting (Fig. 3) dietary assessment results based on 24 h-Recalls using the CIMI approach with results obtained by analyzing respective survey data using NutriSurvey®. Pearson correlations (r ≥ 0.75) and Bland-Altman plots (agreement limit: ± 1.96SD) indicate the comparability of the two dietary assessment procedures and thus, the validity of CIMI. An overview of the CIMI/Nutrisurvey® calculations on dietary energy, macro- and micronutrient intakes as well as calculation differences for all parameters and subpopulation groups are given in Table 3.

2.2.1. Identification of food groups To identify representative food groups that take regional consumption patterns into consideration, data comprising overall food consumption over an extended period was appropriate. Monthly food intakes were estimated and 23 food groups were identified for the Ashanti region (Table 1). Iron, Zinc and Vitamin A represent key micronutrients involved in Hidden Hunger [6] and therefore, had a greater influence on the specification of food groups than other micronutrients. Food composition tables were used as databases that provided information on the nutrient composition of individual food items [33–35]. Food composition of local food items, e.g., some dark greens, which were not found in the databases were sourced from published data [36]. In avoidance of creating biased food groups, the consumption level as well as the nutrient density of each food item had to be considered. Food items with extremely high consumption levels were separated from items in the same category (e.g. maize and rice were separated from cereals, because their consumption within the cereal group was ≥50%). Furthermore, food items with high contents of particular nutrients were separated as well (e.g. fruits were categorized in provitamin A-rich fruits and other fruits). Nutrient profiles of each food group were determined by calculating average values of respective nutrients. This was achieved using following formulas:

ci =

2.2.3. Assessment of iron and zinc bioavailability Uptake of minerals and their bioavailability is affected by diverse factors. Anti-nutrients, e.g. phytic acid, oxalate, tannins or saponins, are chelating and binding minerals, reducing their gastrointestinal absorption, whereas proteins might enhance their bioavailability by formation of complexes. In addition, due to different absorption mechanisms, heme iron from meat and fish possesses a higher bioavailability than non-heme iron. Finally, calcium and phytic acid intake might additionally suppress the absorption of iron and zinc. Therefore, the program works with an algorithm that evaluates the bioavailability of iron and zinc based on different scenarios [32], leading to a more precise assessment of adequate dietary iron and zinc intakes. The algorithm considers the percentage amount of energy being consumed from protein of meat, fish and other animal-based food as well as the percentage amount of energy being consumed from phytic acid-rich sources and the reducing effect on the bioavailability of iron and zinc due to calcium intake (Table 2).

Ci COV

ci: consumption factor of food item i Ci: consumption share of food item i within respective food group COV: overall consumption of food items within respective food group

2.2.4. Comparison with RNI requirements Immediately after entering the 24 h-Recall consumption data into the program structure, CIMI provides information on overall nutrient intakes. These findings are automatically compared to RNI requirements [32]. Furthermore, CIMI distinguishes between the nutritional needs of individual subpopulation groups, additionally considering iron and zinc bioavailability based on regional dietary patterns. In case of inadequate nutrient intakes, dietary gaps are easily identified in respect of the limiting food sources. These results can ultimately be used to expand dietary diversity and give further recommendations to improve individual diets to close potential dietary gaps.

n

∑ ci ∙ni = c1 ∙n1 + …+cn ∙nn i=1

Σ: nutrient profile of respective food group ci: consumption factor of food item i ni: nutrient content of food item i However, some food items were excluded from the calculations, since their consumption level was found to be negligible – consumed less than once in 3 months. Nutrient profiles for energy and the following macro- and micronutrients were obtained: protein, fat, 18

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Fig. 2. Pearson correlations between CIMI and NutriSurvey® of dietary energy, protein, iron, zinc and vitamin A intake for men (A; n = 89), women (B; n = 121) and children under-five (C; n = 83).

3. Results

49% of total protein intake respectively, cereals and animal-based products represented the most important sources for proteins in local diets. Surprisingly, men and women consumed comparable amounts of meat, fish and eggs, whereas the consumption of organ meat had to be considered as negligible in all subpopulation groups. Three quarters of total fat supply was covered through the consumption of vegetable fats. In this context, red palm oil, fortified processed vegetable oil and margarine were identified as rich sources not only for fats but also for vitamin A and E. Not surprisingly, red palm oil accounted for more than half of the total vitamin A intake (Table 4; conversion factor β-carotene: 1:12). Furthermore, plantain (87 μg RAE/100 g) [36] was considered as a valuable complementary source for vitamin A supply. Due to its overall high consumption, starchy staples, more particularly cereals, represented the food group that provided most to dietary B-vitamin and

Dietary patterns in Ashanti region were assessed based on the FFQ results. Diets are strongly characterized by the consumption of starchy staples (54%; e.g. cassava, maize, rice, plantain etc.). In addition, substantial amounts of fruits (21%) and vegetables (17%) as well as considerably fair amounts of meat, fish and eggs (4%) are consumed. Vegetable oils further improve the dietary diversity within the region. However, the consumption of dairy products was considered as marginal. Based on 24 h-Recall results, starchy staples accounted for over 60% of total energy intake. Whereas a comparable contribution of roots and tubers, plantains and cereals was observed for adult men and women, a cereal-based diet was identified for children under-five. With 30 and 19

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Fig. 3. Pearson correlations and Bland-Altman-Plots for thiamin (A), niacin (B) and cobalamin (C) for women (n = 121).

CIMI providing specific food groups that are combined with standardized local portion sizes significantly simplifying and improving data entry during the survey. Once the program is validated, it can be used for large-scale surveys within the region of focus. Results will be obtained in real-time directly after the interview, highlighting energy and nutrient adequacies in comparison with RNIs as well as recommended food sources to combat potential dietary gaps. Therefore, no further data analysis has to be done and a prompt feedback, comprising opportunities for potential interventions with respect to individual dietary habits, can be provided to improve and optimize the diets of the subpopulation groups of interest. The immediate feedback function visualized through a graphical summary of dietary energy, macro- and micronutrient intakes can have an additional beneficial effect of motivating individual respondents to effect improved dietary changes, thus, increasing the compliance for interventions.

mineral intake. Although being poor sources for most micronutrients, > 60% of thiamin, pyridoxin, iron and magnesium, and almost 50% of riboflavin, niacin, folic acid, calcium and zinc were obtained through starchy staples. However, legumes as well as meat, fish and eggs represented substantial additional sources for micronutrients within the Ashanti setting. Whereas fish and other seafood were by far the primary sources for cobalamin and vitamin D, the overall intake of ascorbic acid was covered by almost two-thirds through the consumption of cassava, other tuber-crops and plantain emphasizing the high intakes of these food items. The CIMI findings on individual dietary assessments provide a clear description regarding energy, macro- and micronutrient intakes (Table 4). For the evaluation of dietary micronutrient intakes, the median was used as a reference value, since both the KolmogorowSmirnow and the Shapiro-Wilk test predominantly indicated no normal distribution of the analyzed datasets. Furthermore, thresholds were defined to categorize dietary intake levels. An “adequate” intake was defined to be achieved, if the median was equivalent to ≥100% RNI, whereas “moderate risk”, “risk” and “high risk” intake levels corresponded to > 75–100% RNI, > 50–75% RNI and ≤50% RNI, respectively. Based on the study results, dietary intakes of most micronutrients were largely met in Ashanti region. Magnesium, vitamin E, thiamin, pyridoxine, cobalamin and ascorbic acid were all categorized as adequate in all subpopulation groups. However, dietary intake levels for calcium were generally at high risk (< 36% RNI) and riboflavin (59.7–69.2% RNI) as well as folic acid (69.5–74.9% RNI) intakes in general at risk. Intakes of vitamin A (62.4% RNI) and D (48.0% RNI) in children under-five were identified as at risk and high risk, respectively; whereas the dietary iron (60.1% RNI) intake of women in reproductive age was classified as at risk. Moderate risks were observed for vitamin A (97.8% RNI) in men and zinc (89.6% RNI) in children under-five. Both, women and children under-five had a moderate risk for inadequate intake levels of niacin (90.6% and 76.6% RNI, respectively).

4.1. Data collection This study represents the first to use primary data on FFQs and 24 hRecalls for the use of the CIMI approach. A previous study used socioeconomic survey data on commonly consumed foods in Indonesia to identify specific dietary patterns and adapt CIMI for an Indonesian setting [28]. Data on household-level consumption were collected using FFQs. Thereby, the frequency of consumption was categorized per day, week or month. Measures were taken to reduce errors for the assessment of food intakes during the FFQ interviews. In determining the total household consumption per month, usual consumption per day was first asked. Based upon this, the respondent was guided to estimate weekly average consumption and subsequently, total monthly consumption. Care was taken not to assume monthly consumption is fourfold the weekly consumption. The average weekly consumption only served as a guide for the respondent to estimate monthly consumption. These measures ultimately reduced over- or under-reporting of total monthly food consumption, which is used in identifying food consumption patterns. These patterns as determined through FFQs were crucial as they provide grounds for the creation of food groups, forming the basic structure of CIMI. The purpose of this grouping is to reduce interview time by arriving at an optimum number of food groups for accurate dietary intake assessment. Each group provides a basis to calculate nutrient intakes based on the proportions of food items consumed in the group and their individual nutrient profiles from food composition tables. The food groups in turn, are factors that influence the correlation of CIMI to NutriSurvey®. However, by calculating

4. Discussion CIMI – once adapted to a specific region or country – facilitates data collection and evaluation through its simplified interface based on food groups. Thus, it allows a fast and valid assessment of dietary energy, macro- and micronutrient intakes based on regional consumption patterns. In addition, CIMI features a unique algorithm that assesses iron and zinc bioavailability taking regional dietary patterns into account. Time-savings will be based on the user-friendly program structure of 20

21

277 ± 137 19.9 ± 11.6 509 ± 313 11.5 ± 6.64

347 ± 150 19.2 ± 9.71 523 ± 276 11.8 ± 6.26

1127 ± 1259 264 ± 577 9595 ± 12,834 10.5 ± 8.66 13.5 ± 7.31

1.77 ± 1.21 0.99 ± 0.57 18.4 ± 10.4 2.86 ± 1.67 449 ± 383 5.57 ± 4.34 152 ± 110

Minerals Calcium [mg/d] Iron [mg/d] Magnesium [mg/d] Zinc [mg/d]

Fat-soluble vitamins Vitamin A [μg/d] Retinol [μg/d] β-Carotene [μg/d]⁎ Vitamin D [μg/d] Vitamin E [mg/d]

Water-soluble vitamins Thiamin [mg/d] Riboflavin [mg/d] Niacin [mg/d] Pyridoxine [mg/d] Folate [μg/d] Cobalamin [μg/d] Ascorbic Acid [mg/d] 0.04 ± 0.29 −0.08 ± 0.20 −2.12 ± 4.65 −0.09 ± 0.36 5.76 ± 35.0 1.97 ± 2.21 −21.7 ± 28.4

−7.64 ± 29.4 3.71 ± 11.2 −136 ± 328 5.13 ± 4.99 1.67 ± 4.09

70.4 ± 66.2 −0.68 ± 3.94 13.9 ± 104 0.30 ± 1.75

−7.82 ± 117 0.04 ± 5.64 0.32 ± 13.3 −3.09 ± 9.74 1.34 ± 3.01

Δ (CIMI-NS) Mean ± SD

1.56 ± 0.97 0.81 ± 0.42 15.2 ± 8.78 2.42 ± 1.29 369 ± 254 4.04 ± 3.79 143 ± 88

1087 ± 1111 190 ± 130 10,198 ± 13,276 8.05 ± 8.32 13.9 ± 6.80

310 ± 135 16.7 ± 7.42 460 ± 212 10.0 ± 4.83

2259 ± 819 70.8 ± 34.7 57.4 ± 35.4 349 ± 125 33.6 ± 17.4

CIMI Mean ± SD

1.57 ± 1.07 0.86 ± 0.47 17.5 ± 11.6 2.49 ± 1.37 365 ± 253 2.42 ± 2.22 168 ± 93.5

1096 ± 1109 188 ± 132 10,323 ± 13,227 4.37 ± 4.45 12.5 ± 7.50

253 ± 126 16.8 ± 8.62 434 ± 235 9.55 ± 4.98

2276 ± 836 71.1 ± 35.6 58.2 ± 37.8 351 ± 126 32.2 ± 17.8

NS Mean ± SD

Women (15-50y; n = 121)

−0.02 ± 0.33 −0.05 ± 0.13 −2.25 ± 5.67 −0.07 ± 0.32 3.90 ± 41.2 1.62 ± 1.92 −25.0 ± 25.9

−8.44 ± 34.1 2.16 ± 6.34 −124 ± 388 3.68 ± 4.82 1.44 ± 4.41

57.3 ± 56.3 −0.10 ± 3.64 25.7 ± 107 0.43 ± 1.20

−16.9 ± 114 −0.25 ± 7.31 −0.84 ± 12.8 −2.56 ± 5.68 1.50 ± 3.40

Δ (CIMI-NS) Mean ± SD)

0.71 ± 0.40 0.40 ± 0.28 6.72 ± 3.45 0.98 ± 0.49 187 ± 141 1.87 ± 2.36 49.8 ± 34.7

575 ± 858 204 ± 566 4234 ± 6313 3.42 ± 3.70 8.51 ± 4.46

139 ± 61.9 8.46 ± 3.96 236 ± 118 4.83 ± 2.19

1112 ± 419 34.5 ± 15.4 33.1 ± 16.7 162 ± 67.7 15.8 ± 8.62

CIMI Mean ± SD

0.75 ± 0.48 0.45 ± 0.29 7.77 ± 4.89 1.09 ± 0.62 189 ± 145 1.24 ± 1.97 74.7 ± 50.7

583 ± 854 203 ± 566 4345 ± 6252 1.81 ± 1.70 7.81 ± 4.13

122 ± 71.0 8.77 ± 5.09 227 ± 135 4.76 ± 2.19

1156 ± 411 35.5 ± 16.2 32.9 ± 16.6 172 ± 67.8 15.3 ± 9.29

NS Mean ± SD

Children (1-5y; n = 83)

* Provitamin A-active carotenoids were converted into retinol using conversion factors of 1:12 (β-carotene) and 1:24 (other provitamin A-active carotenoids).

1.73 ± 1.23 1.07 ± 0.64 20.5 ± 11.3 2.94 ± 1.73 443 ± 372 3.62 ± 3.07 173 ± 114

1134 ± 1256 260 ± 578 9729 ± 12,815 5.47 ± 4.24 11.9 ± 6.51

2401 ± 1011 86.3 ± 42.6 58.7 ± 36.3 365 ± 175 35.0 ± 22.4

NS Mean ± SD

Energy and macronutrients Energy [kcal/d] 2329 ± 1655 Protein [g/d] 86.3 ± 42.5 Fat [g/d] 59.1 ± 33.4 CHO [g/d] 362 ± 176 Fibre [g/d] 36.3 ± 21.9

CIMI Mean ± SD

Men (19-65y; n = 89)

−0.04 ± 0.28 −0.04 ± 0.07 −1.05 ± 2.60 −0.12 ± 0.30 −1.67 ± 33.0 0.63 ± 0.92 −24.9 ± 33.4

−8.60 ± 23.7 0.49 ± 2.14 −112 ± 286 1.61 ± 2.17 0.70 ± 2.49

16.9 ± 42.1 −0.31 ± 2.77 8.86 ± 77.9 0.06 ± 0.65

−44.0 ± 107 −1.00 ± 3.37 0.24 ± 6.11 −10.4 ± 21.7 0.49 ± 3.85

Δ (CIMI-NS) Mean ± SD

Table 3 Average dietary intakes of energy, macro- and micronutrients calculated with NutriSurvey® and CIMI for men, women and children under-five as well as calculation differences for all parameters and subpopulation groups.

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Table 4 CIMI calculations for dietary energy, macro- and micronutrient intakes and percentage RNI coverage of uptakes indicated as median (25th/75th percentiles) for men, women and children under-five. Men (19-65y; n = 89)

Women (15-50y; n = 121)

Children (1-5y; n = 83)

% RNI Median (IQR)

CIMI Median (IQR)

% RNI Median (IQR)

CIMI Median (IQR)

% RNI Median (IQR)

Energy and macronutrients Energy [kcal/d] 2329 (1655; 3073) Protein [g/d] 79.3 (55.2; 115) Fat [g/d] 53.2 (33.7; 80.9) CHO [g/d] 355 (237; 472) Fibre [g/d] 31.6 (19.7; 46.9)

– – – – –

2189 (1647; 2744) 67.4 (45.9; 89.8) 51.6 (33.7; 74.2) 340 (253; 442) 29.8 (21.9; 44.5)

– – – – –

1103 (824; 1376) 34.3 (22.5; 45.5) 32.2 (19.8; 42.9) 154 (113; 207) 14.1 (10.6; 20.1)

– – – – –

Minerals Calcium [mg/d] Iron [mg/d] Magnesium [mg/d] Zinc [mg/d]

351 (233; 428) 17.5 (11.6; 25.1) 445 (313; 703) 10.9 (6.70; 15.3)

35.1 (23.3; 42.8)d 164 (96.4; 248) 171 (120; 270) 160 (101; 228)

299 (219; 398) 15.6 (11.3; 21.4) 423 (305; 588) 9.15 (6.73; 12.8)

29.9 (21.9; 39.8)d 60.1 (38.4; 89.4)c 192 (139; 267) 183 (125; 261)

135 (92.0; 168) 7.66 (5.26; 11.4) 223 (141; 318) 4.50 (2.88; 6.26)

22.5 (15.3; 27.9)d 131 (69.0; 200) 293 (185; 418) 89.6 (55.4; 138)b

Fat-soluble vitamins Vitamin A [μg/d] Retinol [μg/d] β-Carotene [μg/d]a Vitamin D [μg/d] Vitamin E [mg/d]

587 (292; 1689) 171 (85.5; 316) 2125 (598; 14,398) 9.84 (2.40; 15.2) 12.5 (7.87; 19.3)

97.8 (48.7; 281)b – – 197 (48.0; 303) 125 (78.7; 193)

526 (289; 1771) 155(92.0; 282) 2119 (542; 17,738) 7.58 (1.13; 15.2) 13.3 (8.34; 18.8)

105 (57.9; 354) – – 152 (22.7; 303) 177 (111; 251)

281 (160; 728) 144 (69.3; 210) 699 (252; 7192) 2.40 (0.28; 4.90) 7.74 (5.35; 11.0)

62.4 (35.6; 162)c – – 48.0 (5.67; 98.1)d 155 (107; 220)

Water-soluble vitamins Thiamin [mg/d] Riboflavin [mg/d] Niacin [mg/d] Pyridoxin [mg/d] Folate [μg/d] Cobalamin [μg/d] Ascorbic Acid [mg/d]

1.42 (0.90; 2.20) 0.90 (0.57; 1.18) 17.5 (11.0; 23.1) 2.54 (1.72; 3.97) 299 (211; 557) 5.69 (1.76; 7.51) 126 (73.7; 189)

119 (75.3; 183) 69.2 (43.6; 90.7)c 110 (68.5; 144) 196 (132; 305) 74.9 (52.7; 139)c 237 (73.2; 313) 281 (164; 421)

1.27 (0.87; 1.92) 0.69 (0.50; 1.03) 12.7 (9.42; 17.9) 2.05 (1.49; 3.34) 278 (205; 459) 3.48 (1.08; 6.95) 139 (73.6; 191)

116 (79.1; 175) 62.8 (45.3; 94.0)c 90.6 (67.3; 128)b 158 (114; 257) 69.5 (51.4; 115)c 145 (45.2; 290) 309 (164; 425)

0.62 (0.43; 0.92) 0.36 (0.21; 0.51) 6.13 (3.97; 8.88) 0.95 (0.59; 1.23) 145 (102; 224) 1.74 (0.54; 2.62) 45.2 (26.5; 68.3)

104 (71.4; 153) 59.7 (34.6; 85.1)c 76.6 (49.6; 111)b 158 (98.1; 206) 72.4 (51.2; 112)c 145 (45.0; 218) 151 (88.3; 228)

CIMI Median (IQR)

a b c d

Provitamin A-active carotenoids were converted into retinol using conversion factors of 1:12 (β-carotene) and 1:24 (other provitamin A-active carotenoids). Moderate risk. Risk. High risk.

evaluate a possible bias between them, Bland-Altman Plots were generated and compared with respective correlations [37]. Three scenarios were identified during the plotting of the Bland-Altman graphs. For all three scenarios, CIMI and NutriSurvey® provided comparable results, particularly at intake levels below the recommendations. However, dispersions at higher intake levels were observed leading to a trendless-, under- or over-reporting of CIMI results as shown for thiamin, niacin and cobalamin, respectively (Fig. 3). On the whole, intake calculations are nevertheless located within, a priori, defined limits of agreement ( ± 1.96SD). Therefore, the increased dispersion at high intake levels are not crucial for the determination of nutrient inadequacies. However, numerical comparison of both methods, also suggest deviating calculations for specific micronutrients (Table 4). Ascorbic acid, vitamin D and cobalamin represent nutrients showing a potential bias. Compared to NutriSurvey®, CIMI calculates dietary ascorbic acid intakes significantly lower; unlike vitamin D and cobalamin for which over-reportings are observed. These discrepancies represent a trade-off and were the consequence of the study focus on a very precise analysis of iron, zinc and vitamin A. Nevertheless, this outcome can be traced to the use of average values of nutrient profiles. These depend on respective consumption factors and nutritional contents of individual food items within the respective food groups. Unless the practical operation of CIMI is not affected, an expansion of food groups in future updates might serve as a solution to counteract the deviations from Nutrisurvey®. Whereas an advanced categorization of vegetables, fruits and legumes is expected to outbalance biases for ascorbic acid, a remodeling of the fish and seafood group will optimize vitamin D and cobalamin assessments. Another approach that is currently being discussed, comprises the implementation of a selection system enabling the (de)selection of food items (e.g. not available, not liked, high price) during the interview likewise offsetting the described bias.

average values for the nutrient profiles of respective food groups, approximations to the real individual values must be expected. The 24 h-Recall is essential for calculating the individual dietary nutrient intake per day using the CIMI computations. In conducting 24 h-Recall interviews, two alternatives were available: probing to assist the respondent determine the amount in grams of individual food items in each mixed diet taken in a day; or using an adapted local food recipe to determine the amounts of individual ingredients taken in grams based upon the quantity of mixed meal taken in a day. The first approach is very challenging to implement when the respondent did not prepare the meal by himself especially with soups, stews and snacks. In the event that they prepared by themselves, estimating the amount of each ingredient of the particular food eaten becomes a huge task even with respondents experienced in cooking. In order to make the program useful to individuals who are unable to arrive at a reasonable estimate of these quantities, the second approach, in which amounts in grams were later estimated based upon the portion sizes of mixed meals consumed and the proportions of ingredients from a Ghanaian recipe book [30] per specified portion sizes, was more appropriate under these survey conditions. However, there can be minor variations in recipes, due to individual preferences or socioeconomic status. Such variations have much more to do with the animal-based food component of the meal rather than the stew, broth or staple food component. Since the animal-based food component was separately considered in data capture in this study, variations in recipes are expected to be minimal. 4.2. Method validation Pearson correlation coefficients (r ≥ 0.75) between CIMI and NutriSurvey® showed excellent agreements and were used as a first measure to compare the new approach with a standardized procedure. As another indicator to assess the comparability of the two methods and 22

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economy. Thus, individual nutrition practices of population subgroups can be optimized and healthier lifestyles promoted representing further steps to improve food security.

4.3. Dietary assessment Based on the CIMI survey findings, only half of women covered up to 60% of the RNI for iron through their diet. As reduced iron intakes leads to anemia, this corroborates with findings of the Ghana Demographic and Health Survey where about 42% of women of reproductive age were found to be anemic [24]. 16% of total dietary iron intake in women was obtained through animal-based food, however, the over dependency on starchy staples and thus, the reduced mineral bioavailability could be accountable for the lower iron coverage. Despite its importance for human health, the significance of an adequate zinc supply has largely been unrecognized [38]. Still, data on dietary zinc intakes and respective inadequacies are very scarce. Yet, the global prevalence of zinc deficiencies could recently be estimated, ranking Ghana at a moderate risk for inadequate zinc intakes [27]. Based on the defined risk thresholds for CIMI, this result was confirmed for children under-five; whereas adequate dietary zinc intakes were identified for both, men and women. Nevertheless, the recent micronutrient survey in Ghana did not provide prevalence on zinc deficiency [25] and therefore, further monitoring is crucial to establish a sufficient data situation for dietary zinc intake levels. Unfortunately, larger datasets on vitamin A coverage in Ghana are not available [26], but CIMI results indicate an adequate supply of vitamin A in most adults, mainly based on high consumption levels of (pro) vitamin A-rich vegetable oils. In addition, about 68% of postpartum women receive vitamin A supplements [24]. However, children under-five might remain a risk group with only about a half, reaching an RNI coverage of 62%. Due to limited datasets on vitamin A uptake through diets, there is an urgent need to expand and verify current findings on dietary vitamin A intakes. Key micronutrients involved in hidden hunger, e.g. iron, zinc and vitamin A, constitute the focus of most studies investigating micronutrient deficiencies. As a result, inadequate intakes of other micronutrients might, thus, be overlooked. This study observed reduced median calcium intakes of < 40% reported for men, women and children largely due to low consumption of dairy products. Though overreporting of vitamin D was observed, uptake for children was low, with about half consuming up to 48% of RNI. However, data on prevalence of calcium and vitamin D inadequacy is not available, presumably because clinical manifestations are uncommon, a clear indication of hidden hunger. In addition, riboflavin and folic acid median intakes ranged from 60 to 69% and 70–75% RNI, respectively. This is in accordance with reported inadequate intakes of riboflavin in the Eastern region [39]. Furthermore, a study in Northern Ghana identified dietary gaps in calcium, cobalamin and ascorbic acid [40]. These inadequacies give a snapshot on the extent of hidden hunger in Ghana and many human growth and developmental outcomes that can be potentially affected especially in women and children under-five. It is worth noting that the CIMI data indicated low individual consumption levels of dark green leafy vegetables (< 10 g/day) for all subpopulation groups. Dark greens however, represent the third richest food group for both B-vitamins, with 100 g providing 40 and 30% RNI for riboflavin and folic acid, respectively, in women. Based on the defined portion sizes, this is equivalent to approximately three ladles of kontomire (Cocoyam leaves) stew. Although, Ghana has made steady improvements in the socio-economic sector, recent data on the impact of the national nutrition policy [41] as well as supplementation programs [41–43] on micronutrient intakes remain scarce [25]. Although there is an indirect monitoring of, e.g., iron deficiency through anemia prevalence levels [24], tracking of other micronutrients is limited. Therefore, appropriate monitoring tools based on actual food consumption are needed to uncover hotspots of hidden hunger and subsequently, to increase both data quality and quantity. Expanded datasets will finally contribute to the identification of adequate intervention measures, e.g., targeted cultivation of food crops, aiming to influence product lines, markets and finally the

4.4. Limitations A preliminary market survey of this study was conducted to determine the main food items available in Ashanti region as well as their respective local units of measure. Results of this survey were used to compile a list of 107 food items that were queried during the FFQ. However, food items that were rarely consumed (defined as: less than once in three months), were excluded from data analysis, because their consumption was considered as insignificant. Since the surveys were not repeated for the two major seasons - wet and dry season - it was further difficult to estimate the impact of seasonal variabilities on consumption patterns. In particular, underutilized food items (e.g. kwawu nsusua – Solanum torvum) and ready-made food products (e.g. milk powder) remain suboptimal due to incomplete nutrient profiles and package labeling, respectively. 4.5. Future developments within the CIMI concept Despite the urgent need for consistent dietary assessment procedures, appropriate approaches are still not available [13,14]. To standardize the described methodology and facilitate cross-country comparisons, the CIMI concept is currently used in the development of an android app (Fig. 4) for rapid dietary assessments including a real-time evaluation of the respondent's intakes versus the recommended requirements [32] based on his/her gender, age and reproductive status. These features will also be available offline, improving CIMI's applicability in rural areas, where internet connection is limited. Interview results will be stored locally on the android devices and synchronized to a central database when connected to the internet. Thus, survey results can be downloaded for further analysis. To enable the usage of CIMI in different settings, it will be designed in a way that strictly separates the country-specific data (e.g. food items, portion sizes, consumption factors etc.) from the core logic for energy, macro- and micronutrient calculation as well as user interface and server synchronization components. Configurations (=“localizations”), which are managed in the form of spreadsheets, are holding all country-specific data such as locally available food items, their nutritive profiles, food groups, locally known units and/or vessels to quantify weights and volumes and their conversion into grams. A new CIMI which is adapted to a specific setting will be created through an upload of such localization-sheets to the CIMI web server where it is subsequently processed into a downloadable country-specific version of CIMI. Thus, the adaptation process of CIMI to a different country or region can easily be realized, whenever the necessary data is collected and formatted according to the standardized requirements given in the core file [44]. 5. Conclusions In this study, an innovative, computer-based approach for individual dietary energy, macro- and micronutrient intake assessment within a Ghanaian region was successfully validated and applied. Due to the user-friendly data entry system consisting of customized food groups and the real-time analysis of survey findings, the program will prove to be fast while ensuring a comparably high precision. In addition, CIMI features a categorization of iron and zinc bioavailability based on regional dietary patterns. CIMI results indicate a largely adequate dietary intake of micronutrients. However, calcium intakes are low in all subpopulation groups, whereas the intake of riboflavin and folic acid was classified as at risk. Vitamin A and D as well as iron represent further micronutrients noticeably not meeting RNIs in children under-five and women of reproductive age, respectively. The CIMI concept clearly distinguishes itself from other dietary 23

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Fig. 4. Screenshot demonstration of the future CIMI android app. The program contains a GPS supported household localization (A). Data on food consumption is entered through slide bars using a list of customized food items and groups and their respective portion sizes (B). Pictures of individual food items provide additional information for the CIMI users assisting them to recall food consumption for the period examined. Finally, program findings are presented as a bar diagram in reference to RNIs (C); thereby, thresholds are visualized through a traffic light system.

Acknowledgments

intake assessment approaches due to the real-time survey analysis including the assessment of energy and nutrient adequacies based on RNIs as well as a prompt feedback option recommending suitable interventions to counteract potential dietary gaps. As such, regional guidelines can easily be developed on both the population and individual level to identify appropriate local food sources and proportions for an improved nutrition. Thus, CIMI represents a first step towards the collection of large-scale datasets on individual dietary intakes based on actual food consumption with respect to regional food patterns and might prove itself as a valid technique for standardized dietary assessments across countries. Such data will serve as a profound basis for stakeholders (e.g. farmers, nutritionists, economists, governments etc.) to influence product lines, markets and finally the economy. CIMI is currently being developed into an android app. Further features, such as the algorithm to categorize iron and zinc bioavailability, represent valuable additional tools that will improve data quality and management contributing to a better understanding of datasets. As a further measure, individual (de)selection of food items, market prices, taste preferences and food taboos as well as a GPS tracked mapping tool comparing nutrient intakes on local level with, e.g., soil quality, are under discussion and might be implemented.

This study was conducted within the GlobE project BiomassWeb (http://biomassweb.org/) funded by the German Federal Ministry of Education and Research (BMBF). Furthermore, the authors acknowledge the support of the German Federal Ministry of Economic Cooperation and Development (BMZ), the field interviewers from the Kwame Nkrumah University of Science and Technology (KNUST) in Ghana and the team of day-med-concept GmbH (Berlin, Germany) for their expertise. References [1] FAO World Food Summit, Available at http://www.fao.org/wfs/index_en.htm (01.08.2018). [2] FAO, IFAD, UNICEF, WFP, WHO, The State of Food Insecurity in the World 2018. Building Climate Resilience for Food Security and Nutrition.; Rome, (2018). [3] B. Thompson, L. Amoroso, Improving Diets and Nutrition: Food-Based Approaches, FAO and Wallingford, Rome, Italy, 2014. [4] K. von Grebmer, A. Saltzman, E. Birol, D. Wiesmann, N. Prasai, S. Yin, Y. Yohannes, P. Menon, J. Thompson, A. Sonntag, Global Hunger Index: The Challenge of Hidden Hunger; Welthungerhilfe, International Food Policy Research Institute, and Concern Worldwide, Bonn, Washington, D.C., and Dublin, 2014, p. 2014, https://doi.org/ 10.2499/9780896299580:. [5] H.K. Biesalski, R.E. Black, Hidden Hunger. Malnutrition and the First 1,000 Days of Life: Causes, Consequences and Solutions, S. Karger AG, Basel, 2016. [6] H.K. Biesalski, Hidden Hunger, English edition ed., Springer, 2013. [7] Food Balance Sheets, Available at http://www.fao.org/faostat/en/#data , Accessed date: 19 March 2019. [8] J.L. Fiedler, D.M. Mwangi, Improving household consumption and expenditure surveys' food consumption metrics - developing a strategic approach to the unfinished agenda, in: IFPRI (Ed.), IFPRI Discussion Paper 1570, 2016 Washington, D.C.. [9] A. Swindale, P. Bilinsky, Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide, FANTA, 2006. [10] T.J. Ballard, A.W. Kepple, C. Cafiero, The Food Insecurity Experience Scale Development of a Global Standard for Monitoring Hunger Worldwide. Technical Paper, FAO, 2013.

Funding This work was funded by the German Federal Ministry of Education and Research (BMBF).

Declaration of Competing Interest The authors confirm that there are no known conflicts of interest associated with this publication. 24

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