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Background: The role of dietary diversity on blood biomarkers may be significant, but the evidence is limited. Objective: This study assessed the association between dietary diversity and haematological status of children aged 6-59 months controlling for various known confounders. Design: The analysis in this study is based on the 2014 Ghana Demographic and Health survey data.The study involved 2,388 pre-school children aged 6-59 months who constituted the sub-sample for anaemia assessment. Results: The mean haemoglobin concentration (Hb) was 10.2 g/dl ± 1.50 (95 % CI: 10.1 to 10.3), and anaemia prevalence (Hb < 11 g/dl) among children aged 6-59 months was 66.8 % (CI: 63.7 to 69.8). In multivariable logistic regression analysis,continued breastfeeding [Adjusted odds ratio (AOR) = 1.9 (95% CI: 1.19–2.91], 12–23 months of age (AOR = 2.4 (95% CI: 1.40–3.98), having fever in last two weeks (AOR = 1.7 (95% CI: 1.20–2.45, birth interval ≤ 24 months (AOR = 1.9 (1.20–2.84), and poorest wealth quintile (AOR = 2.6 (95% CI: 1.48–4.48) were positively associated with anaemia. Conclusion: The current study showed that factors other than poor dietary diversity predicted anaemia among children aged 6–59 months in Ghana.
The study covered all the 10 administrative regions of Ghana. Ghana shares its northern boundary with Burkina Faso and its eastern boundary with the Republic of Togo and a western boundary with La Cote d’Ivoire. This paper is based on further analysis of data which were collected in the 2014 Ghana Demographic and Health Survey (GDHS) carried out across all 10 regions. The community-based cross-sectional survey included 2388 pre-school children aged 6–59 months who constituted the sub-sample for anaemia assessment. Each region was considered a stratum, from which representative probability samples were selected by Demographic and Health Survey (DHS) using stratified cluster sampling methodology. The DHS sample sizes were calculated to account for separate key indicators, and clusters were selected from the master frames in the first stage via the probability proportion to size (PPS) method [16]. Households were then selected from a sampling frame using a random systematic method. Study participants were then interviewed face-to-face by the investigators. Within each selected household, the caregiver responded to questions on anaemia prevention and treatment and expressed her knowledge and practices on anaemia. A pre-tested questionnaire was used to collect information including socio-demographic, infant and young child feeding (IYCF) practices, maternal knowledge, attitude, and practices on iron-rich foods, prevention and treatment of anaemia and child morbidity. The main outcome variable for this study was the prevalence of anaemia (Hb less than 11 g/dl). The independent variables were maternal, child and household characteristics, malarial infection, and child dietary intake. A brief description of main independent and dependent variables is as follows: Haemoglobin levels were determined by using a portable HemoCue 301 photometer. Trained laboratory technicians drew capillary blood samples from the finger prick with a lancet after taking all aseptic precautions. The first drop of blood was wiped away using alcohol sterile wipes, and the next drop was placed into the Hemocue curvette for immediate testing of haemoglobin. According to the World Health Association (WHO), anaemia is defined as the presence of hemoglobin level of less than 11 g/dLin children under five years of age [17]. Anaemia was further classified as mild (9.0–10.9 g/dL), moderate (7.0–8.9 g/dL) or severe (<7.0 g/dL). Anaemia is said to be a severe public health problem when its prevalence is 40% or more in any group (all types of anemia) or when severe anaemia (haemoglobin < 7 g/dL) exceeds 2% [18]. The food groups in the DHS were regrouped to fall in line with the WHO recommended seven food groups used in defining children’s minimum dietary diversity indicator as follows: (i) grains, roots and tubers; (ii) legumes and nuts; (iii) dairy products; (iv) flesh foods (meats/fish/poultry); (v) eggs; (vi) vitamin A-rich fruits and vegetables; and (vii) other fruits and vegetables [13]. Mothers were asked to recall the number of times, in the past 24 hours, a child had received anything to eat, aside from breast milk, including meals and snacks. The dietary diversity score therefore ranged from 0–7 with minimum of 0 if none of the food groups is consumed, to 7 if all the food groups are consumed. WHO defined minimum dietary diversity as the proportion of children aged 6–23 months who received foods from at least four out of seven food groups in a 24 hour time period [13,19]. Traditionally, this concept had been applied to children 6–23 months but in this study, we extended to all children 6–59 months. We defined adequate dietary diversity as consumption of food from at least four different food groups (DDS ≥ 4). Socioeconomic and demographic information was collected on mothers’ age, marital status and highest level of education attained by the mothers. Household socioeconomic status was determined from the household wealth index. The household wealth index is a standardized asset-based score that is divided into quintiles [20]. Additional household variables included household residence (urban/rural) and household size. Mothers were asked if the children were breast feeding at the time of the survey. For morbidity experience, respondents were asked to recall if the child had experienced any diarrhoea or cough episode in the past seven days preceding the interview. Data were analysed using complex samples module for Windows in IBM-SPSS version 20. The analysis of data took into account the complex design of multi-stage cluster surveys. This was done in order to make statistically valid population inferences and computed standard errors from sample data. Sample weights were applied to each stratum to account for differences in population size in each (that is, weighted analysis). Both bivariate and multivariate analyses were carried out to identify risk factors of anaemia. Association between anaemia and some risk factors in pregnancy was tested using chi-square and multivariable analysis of risk factors. Independent variables with p value less than 0.1 in bivariate analysis were entered into multivariable logistic regression model. P value less than 0.05 were taken as statistically significant and adjusted odds ratio with 95% confidence interval (CI) was used to measure association. Analyses of association between haemoglobin concentration (Hb) and other variables were carried out using bivariate and multivariate techniques. First, bivariate analyses for all the various risk factors were performed using chi-square (χ2) tests for categorical variables and analysis of variance (ANOVA) for means of continuous variables. The analyses in this paper are based on secondary data obtained with permission from MEASURE DHS Organization and was downloaded from the Demographic and Health Surveys (DHS) online archive. DHS datasets are in the public domain and available to all registered users who have been granted access upon request. The original DHS data were collected with approval from the Inner City Fund (ICF) International’s Institutional Review Board and national ethical guidelines. Information about objective of the study, procedures, potential risks and benefits was given to mothers before their children were enrolled to the study. Verbal informed consent was obtained before the household questionnaires were administered, and before blood was collected for anemia testing. An informed consent was read in the local language and a copy given to the household upon request. Those selected to give blood samples were informed of the general purpose, possible risks and benefits of the survey in their language. Participation in the survey was voluntary and participants’ full right to refuse participation was explained.
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