Background Vitamin A one of the important micronutrients that it cannot be made in the human body and must be taken from outside the body through the diet. Ensuring that vitamin A is available in any form in sufficient quantities remains a challenge, especially in regions where access to vitamin A-containing foods and healthcare interventions is limited. As a result, vitamin A deficiency (VAD) becomes a common form of micronutrient deficiency. To the best of our knowledge, there is limited evidence on determinants of good Vitamin A consumption in East African countries. Therefore, this study aimed to assess the magnitude and determinants of good vitamin A consumption in East African countries. Methods A recent Demographic and Health Survey (DHS) of twelve East African countries were included to determine the magnitude and determinants of good vitamin A consumption. A total of 32,275 study participants were included in this study. A multilevel logistic regression model was used to estimate the association between the likelihood of good vitamin A-rich food consumption. Both community and individual levels were used as independent variables. Adjusted odds ratio and its 95% confidence interval were used to see the strength of the association. Result The pooled magnitude of good vitamin A consumption was 62.91% with a 95% CI of 62.3 to 63.43. The higher proportion of good vitamin A consumption 80.84% was recorded in Burundi and the smallest good vitamin A consumption 34.12% was recorded in Kenya. From the multilevel logistic regression model, women’s age, marital status, maternal education, wealth index, maternal occupation, children’s age in a month, media exposure, literacy rate, and parity were significantly associated with good vitamin A consumption in East Africa. Conclusion The magnitude of good vitamin A consumption in twelve East African countries is low. To increase good vitamin A consumption health education through the mass media and enhancing the economic status of women is recommended. Planners and implementers should give attention and priority to identified determinants to enhance good vitamin A consumption.
The data was obtained from the measure DHS program at www.measuredhs.com after preparing a concept note about the project. The DHS program collects data across over 90 low- and middle-income countries across the world. The collected data is comparable for each country. The program implemented the same variable code, variable name, manual, data collection tool, and sampling procedure. Therefore, this study was performed according to relevant DHS statistics guidelines [30]. The Demographic and Health Survey (DHS) data were pooled from the 12 East Africa Countries from 2008 to 2017. The recent DHS of Country-specific datasets was extracted during the specified time. The 12 East Africa Countries from which data were extracted include Burundi, Ethiopia, Kenya, Comoros, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe. The DHS program adopts standardized methods involving uniform questionnaires, manuals, and field procedures to gather information that is comparable across countries in the world. DHSs are nationally representative household surveys that provide data from a wide range of monitoring and impact evaluation indicators in the area of population, health, and nutrition with face-to-face interviews of women aged 15 to 49. The surveys employ a stratified, multi-stage, random sampling design. Information was obtained from eligible women aged 15 to 49 years in each country. “The DHS program surveyed according to following sampling procedures. The surveys employ a stratified, multi-stage, random sampling design. First stage: Enumeration Areas (EA) are generally drawn from each country’s Census files. Second stage: in each EA selected, a sample of households is drawn from an updated list of households. Information was obtained from eligible women aged 15 to 49 years in each country. Detailed survey methodology and sampling methods used in gathering the data have been reported elsewhere [31]. A total of 32,275 study participants were included in this study. This include Burundi (4,323), Ethiopia(3,240),Kenya(5,576),Comoros(870),Madagascar(1,725),Malawi(1,641),Mozambique(3,500),Tanzania(1,195),Rwanda(3,082),Uganda(1,429),Zambia(3,811),and Zimbabwe(1,877). Missing data were excluded from the analysis. Living children aged 6–23 months living with their mother who consumed foods rich in vitamin A at least one food item among the seven food items (1) Have the child taken eggs in the last 24 hours?2) Have the child taken meat (beef, pork, lamb, chicken, etc.) in the last 24 hours? 3) Have the child taken a pumpkin, carrots, and squash (yellow or orange inside) in the last 24 hours? 4) Have the child taken any dark green leafy vegetables in the last 24 hours? 5) Have the child taken mangoes, papayas, and another vitamin A fruit in the last 24 hours? 6) Have the child was taken liver, heart, and other organs in the last 24 hours? 7) Have the child taken fish or shellfish in the last 24 hours?) at any time in the last 24 hours preceding the interview was declared good consumption of foods rich in vitamin A coded as “1”, whereas, no consumption of foods rich in vitamin A in the 24 hours preceding the interview was poor consumption coded as “0”. Based on known facts and literature the independent variables included in this was two types of variable that are individual-level and community-level variables. Community-level variables Country and residence. The individual-level variables are Age group, marital status, Educational status, literacy level, Occupational status, parity, children’s age in a month, family size and wealth index, breastfeeding status, and media exposure. The data was cleaned by STATA version 14.1 software. Sample weighting was done for further analysis. Since the outcome variable was binary two-level mixed-effects logistic regression analysis was employed. Sampling weight was applied as part of a complex survey design using the primary sampling unit, strata, and women’s weight (V005). The individual and community-level variables associated with good vitamin A consumption were checked independently in the bivariable multilevel logistic regression model and Variables that were statistically significant at a p-value of 0.2 in the bivariable multilevel mixed-effects logistic regression analysis were considered for the individual and community level model adjustments. A total of four models were fitted. The first was the null model with no exposure variables that were used to verify the variation in the community and give evidence to evaluate random effects at the level of community. The second model was the adjustment of the multiple variable models for individual variables and the third model was adjusted to consider factors at the community level. Whereas, in the fourth model, potential candidate variables from individual and community variables were adjusted to the outcome variable. Fixed effects were used to estimate the association between the probability of good vitamin A consumption explanatory variables at community and individual levels and have been expressed as an odds ratio with a 95% confidence interval. For measures of variation (random effects), the intracluster correlation coefficient (CCI), the proportional variation of community variance (VCP), and the median odds ratio (MOR) were used. The MOR is defined as the median of the odds ratio between the zone of greatest risk and the zone with the lowest risk when two zones are randomly selected. The purpose of the Median Odds Ratio (MOR) is to translate the area level variance in the widely used odds ratio (OR) scale that has a consistent and intuitive interpretation. It is computed by; MOR = exp[√(2×Va)×0.6745] [32] Where; VA is the area level variance, and 0.6745 is the 75th centile of the cumulative distribution function of the normal distribution with mean 0 and variance 1. See elsewhere for a more detailed explanation (24). Whereas the proportional change in variance is calculated as [33] Where; where VA = variance of the initial model, and VB = variance of the model with more terms.