Household food insecurity is associated with child’s dietary diversity score among primary school children in two districts in Ghana

Janet Antwi1, Esi Quaidoo2, Agartha Ohemeng3* and Boateng Bannerman4

1Department of Agriculture, Nutrition and Human Ecology, Prairie View A&M University, Prairie View, TX, USA; 2Department of Nutrition, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA; 3Department of Nutrition and Food Science, University of Ghana, Legon, Accra, Ghana; 4Nutrition Linkages Project, University of Ghana, Accra, Ghana

Popular scientific summary


Background: Dietary diversity is generally considered as a good indicator of nutrient adequacy and is influenced by various factors at the national, household, and individual levels.

Objective: The present study sought to determine the relationships between household food insecurity, primary caregivers’ nutrition knowledge, and dietary diversity of school-aged children in Ghana.

Methods: This forms part of a longitudinal study conducted in the Ayawaso West Municipal district in Accra (urban setting) and the Upper Manya Krobo district (rural setting) in Ghana. Data were collected from a total of 116 caregiver-child dyads using 24-h dietary recall and a short version of the US 12-month Household Food Security Survey Module. Nutrition knowledge and sociodemographic data were obtained using a structured questionnaire. Multivariable logistic regression was used to check for factors associated with children’s dietary diversity.

Results: Majority of households reported food insecurity, with a higher percentage of insecure households located in the rural area (88.9% vs. 46.5%, P ≤ 0.0001), compared to the urban setting. Diet diversity among the study children was low, with a mean (standard deviation [SD]) of 5.8 (2.1) out of 14 food groups. Children living in food insecure households were three times more likely to have received low diverse diet compared to those from food secure households (adjusted odds ratio [OR] =3.3, 95% confidence interval [CI]: 1.4–8.0). Caregivers’ nutrition knowledge was, however, not related to children’s dietary diversity.

Discussion and conclusion: Household food insecurity was a main predictor of dietary diversity among school-age children in this study. Thus, caregiver knowledge in nutrition may not be enough, particularly in the presence of food insecurity to guarantee adequate nutrition for school-aged children.

Keywords: food insecurity; nutrition knowledge; dietary diversity; school-age children


Citation: Food & Nutrition Research 2022, 66: 7715 - http://dx.doi.org/10.29219/fnr.v66.7715

Copyright: © 2022 Anet Antwi et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Received: 8 March 2021; Revised: 25 November 2021; Accepted: 2 December 2021; Published: 7 January 2022

Competing interests and funding: All authors declare no conflict of interest. This work was supported with funding from the Institute of International Education (IIE) through the Carnegie African Diaspora Fellowship Program (CADFP) to JA and AO for the fellowship and funding to undertake this research. The IIE or CADFP had no influence in the study design, conduct of the study, analysis of data, interpretation of findings, or writing of this article. The authors would like to thank the Cooperative Agriculture Research Center of the College of Agriculture and Human Sciences at Prairie View A&M University for supporting cost of publication of this work.

*Agartha Ohemeng, Department of Nutrition and Food Science, University of Ghana, P.O. Box LG 134, Legon, Accra, Ghana. Email: anohemeng@ug.edu.gh


Studies on child nutrition have applied several assessment tools to gauge the presence and extent of malnutrition within different communities (16). One such tool used to date is the dietary diversity score (7, 8). Dietary diversity can be used as a suitable indicator of nutrient adequacy (7, 9), which reflects one’s nutritional status. Research shows that a child who consistently consumes a variety of meals with basic macro- and micro-nutrients is more likely to meet recommended nutrient intakes that help maintain good health (10). Factors that can influence household and individual dietary diversity are varied and include nutrition knowledge (11, 12), socioeconomic factors (1316), cultural factors (3, 17), and food security (3, 13, 18). Knowledge and an understanding of nutrition can play a role in caregivers’ food ingredient sourcing, portion-sizing of the various food groups, and meal preparation techniques (11, 12, 19). Socioeconomic factors such as occupation and level of education have also been identified as determinants of dietary diversity, with lower levels of education correlated with low dietary diversity (2).

Food security has four main dimensions: food availability, economic and physical access to food, food utilization, and stability (20), and thus, it is a concept that is closely linked with a child’s ability to consume a diverse diet. Causes of food insecurity in low-income countries include poverty, ineffective food policies, inauspicious climate events, insufficient food production, and difficulty in accessing food due to poor transportation infrastructure (14, 21, 22).

School-age children (typically 6-12 year olds), and younger children, have a high risk of becoming malnourished when their diets are not optimal (23, 24). Research in child nutrition has largely focused on children below the age of 5 years in Ghana. The few published studies that have documented school-age children nutrition situation suggest that this is another vulnerable group that needs attention if the country is to overcome hunger and all forms of malnutrition (25, 26). Information gathered from such research is needed to assist in constructing contextual food policies aimed at promoting adequate nutrition for school-age children at various administrative levels. Therefore, the objective of the parent study from which data were obtained for this current study was to evaluate the effect of a nutrition education intervention on nutrition knowledge, diet, and nutritional status of school-aged children in urban and rural settings in Ghana (26). The study also engaged the children’s caregivers in assessing nutritional knowledge, attitudes, and practices. This current study focuses on the factors that are associated the dietary diversity scores of school-aged children.


Study design

The analysis presented in this paper focused specifically on primary school children and their caregivers for whom data were available in the intervention arm of a parent study conducted to determine the impact of a 6-week nutrition education intervention on the nutrition knowledge, attitudes, and practices of school-age children in Ghana (26). The participants in the intervention arm were included because only caregivers of children in this arm were included in the parent study, and thus, no food insecurity data were available for children in the control group. The baseline data from the intervention study were used for this paper, to exclude any potential effect of the intervention on the variables of interest.

Study area

This study was conducted in Dzorwulu (urban setting), and Brepaw Upper and Fefe (rural setting) from June through December 2018. Dzorwulu is a vicinity with well-planned residential areas that accommodates the working class and upper crust of Accra society in the Ayawaso West Municipal District, of the Greater Accra Region of Ghana. On the other hand, Brepaw Upper and Fefe are two villages in the Aseseswa subdistrict of the Upper Manya Krobo district in the Eastern Region of Ghana.

Study population and sampling

For this paper on baseline data of the intervention arm, school children from two conveniently selected public basic schools (one urban and one rural) and their primary caregivers were included. The details of sampling and recruitment processes for the parent study have been described in a previous publication (26). Each arm of the parent study consisted of one urban school and one rural school. The sample size for the parent study was calculated using a 95% confidence rate, 4% error margin, and 80% power to detect a 10% difference (P < 0.05) in proportion in nutrition knowledge between intervention and control groups. This gives a minimum required sample size of 86 in each study arm. This was adjusted to 100 per group to account for nonresponses. Caregivers of the children were informed about the study at a Parent Teacher Association meeting, in addition to letters and informed consent forms that were sent home with eligible children for approval. Children within the target age group (6–12 years) who returned signed informed consent forms from their caregivers were included in this study. Caregivers with children within the target age group (6–12 years) who signed informed consent forms for themselves and their children’s participation and gave them to the children to return to the research team were, thus, included in this study.

Data collection

All study questionnaires were pretested among individuals with characteristics similar to the study population to ensure that the assessments were contextual. Thus, questionnaire pretesting took place in two locations: one school located in an urban area and a second school in the rural setting. Research assistants received a 3-day training in implementing and administering the study tools. Sociodemographic information that was obtained included child’s age, gender, and current class in school. Caregivers were invited to their wards’ schools to interact with the research team. Research assistants interviewed caregivers individually; the interview took approximately 30 min. Data on caregivers’ occupation, marital status, level of education, residence, nutrition knowledge, and household food security were also collected. Information on the dietary intake of the school children was also collected.

Caregivers’ nutrition knowledge was obtained through interview questions that focused on number of meals to feed child, what constituted healthy eating, nutrient content of foods, food safety, quality of food to prevent illness, and food function. To generate knowledge scores for each caregiver, a correct answer to each question was assigned a value of one, and an incorrect answer was coded as zero. Next, all scores under this section were summed up, and the total value was used to represent the knowledge score for that person. The maximum possible score for the nutrition knowledge assessment was 20.

The dietary intake data were gathered using single 24-h dietary recall. Each child was asked to list and describe all foods and beverages consumed at school and at home in the past 24-h indicating the time and source of the food. Visual household measures and food models were used to help children estimate the amounts of foods and beverages consumed (data on food quantities are not presented in this paper). The 24-h recall information was used to calculate dietary diversity scores using the Food and Agriculture Organization guidelines (7), by categorizing food items consumed by the children into 14 food groups. For each food group, a child was given a score of one if he/she consumed any food item in that group, and a score of zero if child did not consume any item in the group. A sum of the scores for all the food groups represented the dietary diversity score of participants. To categorize the dietary diversity variable, the median score of the children computed to be six was used as the cut-off point. Thus, low diet diversity in this study was defined as having a total score less than six, and high dietary diversity was defined as a total score equal or more than six.

The household food security status was measured using the 6-item short form of the US 12-month Household Food Security Survey Module (27). The shorter version was used in order to reduce participants’ response burden, in lieu of the longer version. Despite its length, it has been demonstrated to measure and differentiate food security and food insecurity with sufficient sensitivity, specificity, and minimum bias compared to the lengthy module (28). The affirmative responses (‘often true’, ‘sometimes true’, ‘almost every month’, ‘some months but not every month’, and ‘yes’) were coded as ‘1’ and negative responses as ‘0’, and these were added to get a total score for each household. Scores of 0–1 were classified as food secure, while 2–4 and 5–6 were classified as food insecurity without hunger and food insecurity with hunger, respectively. Households were further classified as food secure (score ≤ 1) and food insecure (score > 1).

Statistical analysis

All sociodemographic characteristics of the study participants underwent descriptive analyses. The Pearson’s chi-square test for proportions was first used to evaluate the possible relationship between factors including household food insecurity and dietary diversity, as well as socioeconomic factors including child’s age, sex, and caregiver education. The dependent variable of interest was whether a child received a high or a low diversified diet in the 24 h prior to the interview, and the main independent variable of interest was household food insecurity status. Multivariable logistic regression modeling was conducted after the Pearson chi-square test to assess the relationship between household food insecurity status and child’s dietary diversity, adjusting for caregiver nutrition knowledge, formal education, and sociodemographic factors such as the age and sex of children. The choice of variables that were included in the adjusted analysis was based on existing literature on the possible related factors of individual dietary diversity. Although the ‘location’ variable (urban/rural) was significantly associated with dietary diversity (P < 0.0001) in the bivariate analysis, it was not included in the adjusted model because of the very low level of variability observed in the sample (only two children in the rural setting had high diverse diet). Statistical analyses were performed using SPSS version 20.0, and P-value of <0.05 was considered statistically significant.

Ethical approval

This study was conducted according to the guidelines laid out in the Declaration of Helsinki, and researcher received ethical approvals and permissions from all the relevant institutions before data collection started.


A total of 116 caregiver-child dyads were included in this analysis. The mean age of the children was 9.6 (1.8) years, while that of the caregivers was 38.5 (10.8) years (Table 1). Majority of the caregivers (73.3%) who participated in the study were women, and most participants were the biological mothers and fathers (82.8%) of the study children. Most of the primary caregivers had low level of formal education. Only about one-third of them (31.1%) had gone through the Senior High School level or above. Trading was the most common primary occupation among the caregivers (Table 1), followed by farming and vocational jobs such as dressmaking and hairdressing. It is, however, important to note that with the exception of one participant, all the caregivers who indicated farming as their primary occupation were located in the rural setting.

Table 1. Sociodemographic characteristics of study participants (N = 116)
Characteristic Mean (SD) n (%)
Age (years): 9.61 (1.84)
 6–9 48 (41.4)
 10–12 68 (58.6)
 Male 53 (45.7)
 Female 63 (54.3)
School levela:
 Lower primary 60 (51.7)
 Upper primary 56 (48.3)
Takes money to school: 104 (89.7)
Amount (GH¢): 2.37 (1.85)
 Age (years) 38.89 (10.79)
  Male 31 (26.7)
  Female 85 (73.3)
 Marital status:
  Married/co-habiting 86 (74.2)
  Separated/divorced/widowed 15 (12.9)
  Single 15 (12.9)
 Formal educationb:
  None 23 (19.8)
  Primary 16 (13.8)
  JHS 41 (35.4)
  SHS 27 (23.3)
  Above SHS 9 (7.7)
 Primary occupation:
  Trading 40 (34.5)
  Farming 26 (22.4)
  Vocational 26 (22.4)
  Unemployed/student 5 (4.3)
  Otherc 19 (16.4)
 Relation to study child:
  Parent 96 (82.8)
  Other relative 20 (17.2)
 Estimated household income/month (GH¢):
  ≤500 71 (61.2)
  Above 500 35 (30.2)
  Do not know 10 (8.6)
  Urban 71 (61.2)
  Rural 45 (38.8)
aLower primary consists of classes One to Three, while Upper primary is made up of classes Four to Six.
bJHS represents Junior High School, and SHS represents Senior High School in Ghana.
cOther occupations included public servants, pensioners, domestic help, and laborer. Values are presented as frequencies (%) or means (standard deviation).

The experience of household food insecurity was reported by majority of the caregivers (62.9%), with only about one-third being classified as food secure (Fig. 1). Of households that were classified as food insecure, more than half were severely food insecure. Food insecurity was reported by a higher proportion of caregivers in the rural setting (88.9% vs. 46.5%, P ≤ 0.0001), compared to those in the urban setting.

Fig 1
Fig. 1. Household food insecurity among study participants. The darkest section represents study respondents who were food insecure without hunger. The crossed-line section represents study respondents who were food insecure with hunger, and the brick section represents the study respondents who were food secure.

Based on a single 24-h recall, the most consumed food items consumed by the children were from the grains’ food group, followed by the ‘other vegetable’ group such as onions and garden eggs (Fig. 2). Among the animal source foods, the most common type consumed by the study children during the period of assessment was fish, but organ meat was totally absent from their diet. Notably, vitamin A rich fruits were the least consumed in the vegetable and fruit category.

Fig 2
Fig. 2. Intake of food groups by the school children, based on a single 24-h dietary recall. Bars represent the groups of food that the school children ate in 24 h. DGLV stands for dark green leafy vegetables.

Generally, diet diversity among the study children was low, with a mean score of 5.8 (2.1) out of 14 food groups. Using the group median of six as cut-off, about half of the study children (50.9%) consumed high diverse diets within the 24 h prior to the data collection. Bivariate analysis indicated a higher proportion of older children received diverse diet compared to younger children, but there was no difference based on gender (Table 2). Almost all children (96.2%) living in households, where the main occupation of the primary caregiver was farming, received low diverse diet within the period of observation. In a logistic regression model, dietary diversity of the children was significantly associated with household food insecurity and child’s age (Table 3). Children living in food insecure households were three times more likely to have received a low diverse diet, and this was significant for both levels of food insecurity (without and with hunger), compared to children from food secure households. On the other hand, older children were less likely to have eaten a low diverse diet (odds ratio [OR] = 0.8, 95% confidence interval [CI]: 0.6 – 0.9). There was also a tendency for children whose primary caregivers had formal education up to at least Senior High to consume a high diverse diet. There was, however, no significant association between dietary diversity of children and caregivers’ nutrition knowledge.

Table 2. Bivariate analysis comparing study school children based on dietary diversity
Independent variables Child’s dietary diversity P
Total High (n = 59) Low (n = 57)
Child’s sex 0.270
 Male 53 (45.7) 24 (45.3) 29 (54.7)
 Female 63 (54.3) 35 (55.6) 28 (44.4)
Child’s age 0.041
 6–9 years 48 (41.4) 19 (39.6) 29 (60.4)
 10–12 years 68 (58.6) 40 (58.8) 28 (41.2)
Caregiver education 0.022
 Below SHS 80 (69.0) 35 (43.8) 45 (56.2)
 SHS and above 36 (31.0) 24 (66.7) 12 (33.3)
Caregiver occupation 0.002
 Trader 40 (34.5) 23 (57.5) 17 (42.5)
 Farmer 26 (22.4) 1 (3.8) 25 (96.2)
 Vocational 26 (22.4) 17 (65.4) 9 (34.6)
 Unemployed 5 (4.3) 4 (80.0) 1 (20.0)
 Other 19 (16.4) 14 (73.7) 5 (26.3)
Area of residence <0.0001
 Urban 71 (61.2) 57 (80.3) 14 (19.7)
 Rural 27 (38.8) 2 (4.4) 25 (95.6)
Household food security 0.006
 Food secure 43 (37.1) 29 (67.4) 14 (32.6)
 Food insecure 73 (62.9) 30 (41.1) 43 (58.9)
Data presented as frequency (%).


Table 3. Factors associated with child diversity as unadjusted and adjusted odds ratios
Independent variables Low child dietary diversity
Unadjusted Adjusted
OR 95% CI OR 95% CI
Child’s age (years) 0.8 0.7, 1.0 0.8 0.6, 0.9
Child’s sex 0.7 0.3, 1.4 0.7 0.3, 1.6
 Male, femalea
Caregiver education 0.4 0.2, 0.9 0.4 0.2, 1.1
 Below Senior High (SHS) Levela
 SHS and above
Caregiver nutrition knowledge 1.1 0.8, 1.4 1.2 0.9, 1.7
Household food security
 Food securea 3.1 1.2, 8.2 2.9 1.0, 8.4
 Food insecure without hunger 2.9 1.2, 6.9 3.1 1.2, 8.0
 Food insecure with hunger
Using the group median of 6 as cut-off, low diversity was defined as a total diversity score less than 6. Diversity score was calculated according to FAO guidelines (7).
aReference category of the categorical variables §P < 0.10 and *P < 0.05.


Engaging caregivers in an attempt to piece together school children’s nutrition situation is particularly important since key determinants of dietary practices adopted by children include both caregivers’ and household characteristics. Our study engaged caregivers of school aged children in urban and rural Ghana to assess their nutrition knowledge and their households’ food security.

Dietary diversity, in general, was low among both urban- and rural-based school aged children. Consumption of grains was high among our sample of children with rural-based children consuming more grains than any other food group when compared to their urban-based counterparts. This finding is consistent with that from a study in Uganda (29), indicating that grain consumption is generally high among children in sub-Saharan Africa as they form most of the staple foods. Starchy tubers and roots were also largely consumed by both urban- and rural-based children. Starchy foods are an indispensable meal ingredient in many African meals as they seem to be readily available and accessible than other food staff (8, 30). Grains and starches are relatively cheaper than other food groups such as animal protein, and starch-based meals are perceived to be quenchers of hunger (15, 30). On the other hand, low consumption of fruits, vegetables, animal protein, and dairy was observed among participants, similar to other studies (2931). One may expect that with majority of rural households in this study engaged in farming, intake of vegetables in general would be high due to the cultivation of these food items on their farms. However, it has been noted that subsistence farmers in developing countries focus on growing few varieties of crops, which are mainly slated for sale (29, 30). As a result, farming households tend to base their diets on few food groups resulting in low dietary diversity of household members. Additionally, reports indicate that in developing countries, many crops are consumed only when they are in season, particularly in households with low incomes (32, 33). In this study, dark green leafy vegetables (DGLV), a rich source of iron, folate, and beta-carotenes, were not consumed by most of the children. Even though our study did not collect data on seasonal variations in relation to dietary intake, this research took place when common local DGLV was out of season. Considering that most of the households had low incomes, this seasonality might have accounted for the low intake of nutrient-dense dark leafy vegetables among the school children in this study.

Our study identified more than half of the study’s participating households as food insecure, with about a quarter of them identified as being food insecure with hunger, and household food insecurity was strongly associated with child dietary diversity. Earlier studies have reported varied prevalence of food insecurity in different parts of Ghana (18, 34, 35), indicating vast differences with respect to different parts of the country as well as the seasonality of food availability. Additionally, almost all farming households were food insecure in the current study. This is similar to findings from study that assessed household food security in farming and nonfarming communities in three ecological regions in Ghana (18). Many of the rural-based caregivers did not have a steady income, relying largely on the sale of produce from their farms to gain income. In the urban setting, most of the caregivers reported earning monthly incomes of less than 500 Ghana cedis (i.e. 85.8 US dollars). Household wage earners with regular incomes have more purchasing power and may be more likely to purchase food items even when they are out of season and not as common (2) and, thus, can vary the meals consumed at the household level, compared to households where income is not as steady (20). It is also important to note that the observed association between household food insecurity and child dietary diversity in this study has also been noted among younger children (18, 20). Thus, there is the need to include food security issues at the household level as an integral part of policies and strategies addressing nutrition of all children.

There was a tendency for children whose primary caregivers had less than secondary education to receive low diverse meals during the observation period. A study in Algeria reported that the educational level of caregivers played a significant role in the dietary diversity of children in their study (36). Considering that education qualification can influence caregivers’ income (2), the higher a caregiver’s education level, the more likely a household would have high household income, which could translate into a more diversified diet for children within the household.

Caregivers’ nutrition knowledge, however, was not associated with dietary diversity of the school children studied, contrary to other studies (11, 12). It has been suggested that mothers who have knowledge on nutrition have a protective impact on their children’s nutrition status as they are better equipped to make appropriate dietary decisions for their wards (19). However, caregiver knowledge alone is insufficient to result in better diet since other important factors such as food availability and accessibility and intra-household food allocation influence what is finally consumed. For example, milk and milk products were barely consumed by the children in the current study, even though some caregivers knew the benefits of dairy. Thus, for our study sample, caregivers’ knowledge of nutrition did not necessarily translate into diverse diet. This study illustrates that even though an awareness of the advantages of specific foods to children’s health may be present in a caregiver, the availability, accessibility, and utilization of these products would be an issue in a food insecure home. Thus, food insecurity, as observed in this study, can have a greater impact on the dietary choices made for children than parental awareness of adequate nutrition behaviors.


The findings from this study indicate a significant relationship between household food security and dietary diversity of school age children, but caregivers’ nutrition knowledge was not associated with diet diversity of the children. Our study provides additional evidence that various factors such as household level food security play important roles in ensuring good quality nutrition for school-aged children. Programmes aimed at tackling malnutrition among school-aged children should, therefore, tailor interventions that address numerous drivers of child malnutrition including food insecurity at household levels.

Study limitations

These findings should be interpreted with some level of caution. This study establishes possible associations, not causality. Although the researchers used validated questionnaires and rigorous assessment tools, not all factors that could possibly impact a child’s dietary diversity, such as cultural practices and seasonal variations of food availability, were assessed. Additional information on the actual nutrient quality of the meals consumed by the children was not recorded.


JA and AO designed the research. EQ participated in data collection. BB and AO analyzed the data. JA, EQ, and AO wrote the first draft of the manuscript. JA, EQ, AO, and BB reviewed and edited the manuscript. All authors read and approved the final manuscript. We are indebted to the research assistants for their untiring support during data collection. We cherish the school principals, primary school teachers, school children, caregivers, and school cooks/vendors of the four primary schools that participated in the parent study for the immerse contribution and support during the implementation of the study.

Ethical standards disclosure

This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the College of Basic and Applied Sciences, University of Ghana Institutional Review Board (IRB, {study # ECBAS 029/17-18}) and State University of New York at Oneonta IRB (study # 529). Approvals were sought from the Accra Metropolitan Assembly directorates, Upper Manya Krobo District of the Ghana Education Service, and school principals of the four participating schools. A voluntary written informed consent was obtained from all participants.


  1. Arimond M, Wiesmann D, Becquey E, Carriquiry A, Daniels MC, Deitchler M, et al. Simple food group diversity indicators predict micronutrient adequacy of women’s diets in 5 diverse, resource-poor settings. J Nutr 2010 Nov 1; 140(11): 2059S–69S. doi: 10.3945/jn.110.123414
  2. Codjoe SN, Okutu D, Abu M. Urban household characteristics and dietary diversity: an analysis of food security in Accra, Ghana. Food Nutr Bull 2016 Jun; 37(2): 202–18. doi: 10.1177/0379572116631882
  3. Kennedy GL, Pedro MR, Seghieri C, Nantel G, Brouwer I. Dietary diversity score is a useful indicator of micronutrient intake in non-breast-feeding Filipino children. J Nutr 2007 Feb 1; 137(2): 472–7. doi: 10.1093/jn/137.2.472
  4. Steyn NP, Nel JH, Nantel G, Kennedy G, Labadarios D. Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy? Public Health Nutr 2006 Aug; 9(5): 644–50. doi: 10.1079/PHN2005912
  5. Torheim LE, Ouattara F, Diarra MM, Thiam FD, Barikmo I, Hatløy A, et al. Nutrient adequacy and dietary diversity in rural Mali: association and determinants. Eur J Clin Nutr 2004 Apr; 58(4): 594–604. doi: 10.1038/sj.ejcn.1601853
  6. Vandevijvere S, De Vriese S, Huybrechts I, Moreau M, Van Oyen H. Overall and within-food group diversity are associated with dietary quality in Belgium. Public Health Nutr 2010 Dec; 13(12): 1965–73. doi: 10.1017/S1368980010001606
  7. Food and Agriculture Organization. Guidelines for measuring household and individual dietary diversity. Rome: The United Nations; c2010–2013. Nutrition and Consumer Protection Division; [about 60 screens]. Available from: http://www.fao.org/3/a-i1983e.pdf [cited 25 July 2017].
  8. Chagomoka T, Drescher A, Glaser R, Marschner B, Schlesinger J, Nyandoro G. Women’s dietary diversity scores and childhood anthropometric measurements as indices of nutrition insecurity along the urban–rural continuum in Ouagadougou, Burkina Faso. Food Nutr Res 2016 Jan 1; 60(1): 29425. doi: 10.3402/fnr.v60.29425
  9. Ruel MT. Is dietary diversity an indicator of food security or dietary quality? International Food Policy Research Institute (IFPRI) Discussion Paper Brief; 2002. Available from: https://www.ifpri.org/publication/dietary-diversity-indicator-food-security-or-dietary-quality-0 [cited 25 July 2017].
  10. Fanzo, J. The nutrition challenge in sub-Saharan Africa. United Nations Development Programme [UNDP] Working Paper. 2012. Available from: http://www.undp.org/content/dam/rba/docs/Working%20Papers/Nutrition%2 [cited 12 January 2020].
  11. Ali S, Chaudry T, Naqvi QU. Effect of maternal literacy on child health: myth or reality. Ann PIMS-Pak Inst Med Sci 2011; 7: 100–3.
  12. Shanshan GE, Jingqiu MA, Shanshan LI, Jie Zhang XS. Lack of dietary diversity contributes to the gaps in micronutrient status and physical development between urban and rural infants. Iran J Public Health 2018 Jul; 47(7): 958.
  13. Ali NB, Tahsina T, Hoque DM, Hasan MM, Iqbal A, Huda TM, et al. Association of food security and other socio-economic factors with dietary diversity and nutritional statuses of children aged 6–59 months in rural Bangladesh. PLoS One 2019 Aug 29; 14(8): e0221929. doi: 10.1371/journal.pone.0221929
  14. Usfar AA, Fahmida U, Februhartanty J. Household food security status measured by the US-Household Food Security/Hunger Survey Module (US-FSSM) is in line with coping strategy indicators found in urban and rural Indonesia. Asia Pac J Clin Nutr 2007 Jun 1; 16(2): 368–74.
  15. Bukania ZN, Mwangi M, Karanja RM, Mutisya R, Kombe Y, Kaduka LU, et al. Food insecurity and not dietary diversity is a predictor of nutrition status in children within semiarid agro-ecological zones in eastern Kenya. J Nutr Metab 2014 Jan 1; 2014: 907153. doi: 10.1155/2014/907153
  16. Anarfi JK & Ahiadeke C. Improving the health of children among the urban poor in the city of Accra, Ghana. Accra, Ghana: African Population and Health Research Center, ISSER; 2006.
  17. Ochieng J, Afari-Sefa V, Lukumay PJ, Dubois T. Determinants of dietary diversity and the potential role of men in improving household nutrition in Tanzania. PLoS One 2017 Dec 12; 12(12): e0189022. doi: 10.1371/journal.pone.0189022
  18. Christian AK, Marquis GS, Colecraft EK, Lartey A, Soueida R. Household food insecurity but not dietary diversity is associated with children’s mean micronutrient density adequacy in rural communities across Ghana. Nutrition 2019 Sep 1; 65: 97–102. doi: 10.1016/j.nut.2019.03.006
  19. Fadare O, Amare M, Mavrotas G, Akerele D, Ogunniyi A. Mother’s nutrition-related knowledge and child nutrition outcomes: empirical evidence from Nigeria. PLoS One 2019 Feb 28; 14(2): e0212775. doi: 10.1371/journal.pone.0212775
  20. Faber M, Schwabe C, Drimie S. Dietary diversity in relation to other household food security indicators. Int J Food Saf Nutr Public Health 2009 Jan 1; 2(1): 1–5. doi: 10.1504/IJFSNPH.2009.026915
  21. Agbadi P, Urke HB, Mittelmark MB. Household food security and adequacy of child diet in the food insecure region north in Ghana. PLoS One 2017 May 11; 12(5): e0177377. doi: 10.1371/journal.pone.01773377
  22. Sasson A. Food security for Africa: an urgent global challenge. Agric Food Sec 2012 Dec; 1(1): 1–6. doi: 10.1186/2048-7010-1-2
  23. Best C, Neufingerl N, Van Geel L, van den Briel T, Osendarp S. The nutritional status of school-aged children: why should we care? Food Nutr Bull 2010 Sep; 31(3): 400–17. doi: 10.1177/156482651003100303
  24. Danquah AO, Amoah AN, Steiner-Asiedu M, Opare-Obisaw C. Nutritional status of participating and non-participating pupils in the Ghana School Feeding Programme. J Food Res 2012 Aug 1; 1(3): 263. doi: 10.5539/jfr.v1n3p263
  25. Owusu JS, Colecraft EK, Aryeetey R, Vaccaro JA, Huffman FG. Nutrition intakes and nutritional status of school age children in Ghana. J Food Sci 2017; 6(2): 11–23. doi: 10.5539/jfr.v6n2p11
  26. Antwi J, Ohemeng A, Boateng L, Quaidoo E, Bannerman B. Primary school-based nutrition education intervention on nutrition knowledge, attitude and practices among school-age children in Ghana. Glob Health Promot 2020 Dec; 27(4): 114–22. doi: 10.1177/1757975920945241
  27. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to measuring household food security. Alexandria, VA: US Department of Agriculture, Food and Nutrition Services; 2000. Available from: https://www.fns.usda.gov/guide-measuring-household-food-security-revised-2000 [cited 25 July 2017].
  28. Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the Household Food Security Scale. Am J Public Health 1999 Aug; 89(8): 1231–4. doi: 10.2105/AJPH.89.8.1231
  29. Mayanja M, Rubaire-Akiiki C, Morton J, Young S, Greiner T. Diet diversity in pastoral and agro-pastoral households in Ugandan rangeland ecosystems. Ecol Food Nutr 2015 Sep 3; 54(5): 529–45. doi: 10.1080/03670244.2015.1041135
  30. Chakona G, Shackleton C. Minimum dietary diversity scores for women indicate micronutrient adequacy and food insecurity status in South African towns. Nutrients 2017 Aug; 9(8): 812. doi: 10.3390/nu9080812
  31. Leyna GH, Mmbaga EJ, Mnyika KS, Hussain A, Klepp KI. Food insecurity is associated with food consumption patterns and anthropometric measures but not serum micronutrient levels in adults in rural Tanzania. Public Health Nutr 2010 Sep; 13(9): 1438–44. doi: 10.1017/S1368980010000327
  32. Abizari AR, Azupogo F, Nagasu M, Creemers N, Brouwer ID. Seasonality affects dietary diversity of school-age children in northern Ghana. PLoS One 2017 Aug 14; 12(8): e0183206. doi: 10.1371/journal.pone.0183206
  33. Savy M, Martin-Prével Y, Traissac P, Eymard-Duvernay S, Delpeuch F. Dietary diversity scores and nutritional status of women change during the seasonal food shortage in rural Burkina Faso. J Nutr 2006 Oct 1; 136(10): 2625–32. doi: 10.1093/jn/136.10.2625
  34. Ministry of Food and Agriculture (MoFA). Food security situation in Ghana. Ghana: Northern Region Agricultural Development Unit; July 2015. Available from: https://mofafoodsecurity.wordpress.com/food-security-situation-in-ghana [cited 20 July 2020].
  35. Ohemeng A, Marquis GS, Lartey A. Household food insecurity is associated with respiratory infections among 6–11-month-old infants in rural Ghana. Pediatr Infect Dis J 2015 Aug 1; 34(8): 821–5. doi: 10.1097/INF.0000000000000743
  36. Morseth MS, Grewal NK, Kaasa IS, Hatloy A, Barikmo I, Henjum S. Dietary diversity is related to socioeconomic status among adult Saharawi refugees living in Algeria. BMC Public Health 2017 Dec; 17(1): 1–9. doi: 10.1186/s12889-017-4527-x