Background: Anemia is a serious public health problem that occurs when the blood contains fewer red blood cells than normal. In Ethiopia, the prevalence of anemia in pregnancy increased between 2005 and 2016. The aim of this study was to determine what factors influence the anemia status of pregnant women in Ethiopia. Methods: Anemia status in a sample of 1053 pregnant women was studied using data from Ethiopia’s Demographic and Health Survey 2016. Percentages and graphs were used to show the prevalence of anemia. The marginal probability effect was used to determine the contribution of each explanatory variable category to a single response category of anemia level. Ordinal logistic regression models were constructed, and the best-fitting model was selected to reveal significant anemia status variables. Results: The prevalence of anemia in pregnant women was found to be 37.51% (3.04% severe, 17.28% moderate, and 17.1% mild anemic). The fitted partial proportional odds model revealed that anemia status of pregnant women was significantly associated with region afar (OR = 0.45; CI: 0.21–0.96), antenatal care visits above 4 (OR = 1.58; CI: 1.03–2.43), parity between 1–2 (OR = 0.47;CI: 0.26–0.85), iron taking (OR = 3.68;CI: 2.41–5.64), and higher education (OR = 4.75;CI: 2.29–9.85). Conclusions: Anemia among pregnant women has been identified as a moderate public health issue in Ethiopia. The study revealed that the prevalence of anemia varied among regions which the highest (65.9%) and the lowest (9%) being from Somali and Addis Ababa, respectively. As a result, it is argued that treatments target iron consumption, maternal education, antenatal visits, and mothers’ access to health care.
The data for the analysis came from 2016 Ethiopian Demographic and Health Survey (EDHS). It is the fourth comprehensive and nationally representative, cross-sectional, population and health survey conducted by the Central Statistical Agency in collaboration with the Federal Ministry of Health (FMoH) and the Ethiopian Public Health Institute (EPHI) with technical assistance from ICF International, and financial as well as technical support from development partners. The 2016 EDHS sample was stratified into urban and rural areas and then selected in two stages. A total of 645 enumeration areas (EAs) with an average of 181 households were chosen in the first stage, with probability proportional to EA size (202 of them were from urban areas, while 443 were from rural areas). In the second stage, systematic sampling was used to choose 28 households per EA. For the survey, a total of 17,067 households were occupied. A total of 16,650 women were successfully questioned, resulting in a 98 percent response rate. A total of 15,683 women were chosen for the sample from Ethiopia’s nine regions and two city administrations, of whom 1,122 were pregnant and 1,053 were successfully questioned [10]. After receiving approval from the EDHS program, the 2016 EDHS data were obtained from the DHS program website (http://www.dhsprogram.com). Based on the existing literature, data cleaning, extraction, variable selection, and recoding of the classification of some categorical variables were completed. Sampling weights were used to account for unequal selection probability between strata. All pregnant women with known hemoglobin levels were included in this study, while women with unknown hemoglobin levels were excluded. The outcome variable was the anemia status of pregnant women aged from 15 to 49. It was determined based on hemoglobin concentrations in the blood. Anemia was defined as the occurrence of hemoglobin levels less than 11 g/dL. It was further categorized in to severe, moderate, mild and not anemic with hemoglobin ranges < 7.0 g/dl, 7.0—9.9 g/dl, 10.0—10.9 g/dl, and ≥ 11.0 g/dl respectively [10, 11]. According to WHO, the prevalence of anemia should be less than 5% and is defined as a mild public health problem at a prevalence of 5% to 19.9%, a moderate problem at a prevalence of 20% to 39.9%, and a severe problem at a prevalence of 40.0% or more [12]. The selections of explanatory variables were theoretically driven that draw support from prior research with regard to factors affecting pregnant women’s hemoglobin levels. Previous studies have been referenced in creating categories for naturally continuous and discrete variables [13–16] (Table (Table11). Socio-demographic and other characteristics of pregnant Women’s anemia status, EDHS 2016 (n = 1053) We examined the data for completeness and consistency once it was extracted, and then we completed the preliminary analysis. Descriptive and inferential statistics were used to analyze the data. Different tools, such as frequency distributions, percentages, and graphs, were utilized in descriptive statistics to demonstrate the anemia status of pregnant women. To determine the relationship between each explanatory variable and the outcome variable, a Chi-square test was used (anemia status). In the final multivariable logistic regression analysis, factors having a p-value less than 0.15 in the bivariate analysis were included. The variance inflation factors test (VIF < 10) was used to check for multi-co-linearity of the explanatory variables, and no co-linearity was observed between the candidate variables (all the candidate variables had a VIF value of less than 3). The factors of anemia were discovered using the ordinal logistic regression approach. Variables with p-values less than 0.05 were judged to have a statistically significant association with anemia status in the final model. The strength of the link was assessed using an odds ratio with a 95% confidence interval. Data were analyzed using SPSS version 20 and STATA version 15. Logistic regression is the basic and popular modeling approach when the dependent variable is dichotomous or polytomous. When the dependent variable has more than two categories, it may be ordered or unordered. Ordinal logistic regression models are used to model the relationship between independent variables and an ordinal response variable when the response variable category has a natural ordering [17]. The proportional odds model estimates the odds of being at or below a particular level of the response variable. It considers the probability of that event and all events before it. If the proportional odds assumption, i.e., the relationship between the independent variables and the dependent variable, does not change as the dependent variable’s categories is not met, then other different ordinal models are used to identify important explanatory variables. When the proportional odds assumption is met for some but not all explanatory variables, the partial proportional odds model (PPOM) is used, whereas the generalized ordered logit model (GOLM) is used when the proportionality constant can be completely or partially relaxed for the set of explanatory variables [18]. The continuation ratio logistic model (CRM) compares the probability of response to a given category with the probability of higher response. The construction of adjacent-categories logit recognizes the ordering of response variable categories and determines the logits for all pairs of categories [19]. STATA was used to fit all of the above models to the data set (version 15). The variables were chosen with care and from a survey of the literature. The "ologit" command was used to fit the proportional odds model, and then the "Brant" test was used to evaluate the parallel line assumption. For ordinal logistic regression, the model parameters are estimated by the maximum likelihood estimation (MLE) techniques. In general, the method of maximum likelihood produces values of the unknown parameters that best match the predicted and observed probability values. Therefore, it usually uses a very effective and well known Fisher scoring algorithm to obtain ML estimates [20]. In the case of logistic regression, the model selection criteria based on their results, reasonableness, and fit as measured, will be taken as AIC/ BIC. The log-likelihood value of the models is used to compare the ordinal logistic model, i.e., the model with a higher log-likelihood is considered as better fitted. Akaike Information Criterion (AIC) and Baye’s Information Criterion (BIC) are used to compare models, and the model with the smallest absolute AIC and BIC statistic is considered the best model [21]. The overall model fit in ordinal logistic regression is based on the change in minus2 log-likelihood when the variables are added to a model that contains only the intercept. McFadden's pseudo R-squared statistic was used to compute based on the log likelihood for the model with predictors compared to the log-likelihood for the model without predictors [22], and the significance of individual explanatory variables in the model was checked by using the Wald test. The Pearson and deviance goodness-of-fit test was used to measure the goodness of fit for the model. The average marginal probability effects of predictors on a single level of the response variable are not achievable in ordinal logistic regression. For categorical independent variables, marginal effects are easier to understand and utilize than marginal effects for continuous variables. After adjusting for the other factors in the model, the ME for categorical variables shows how P(Y) varies as the categorical variable moves from one to the other. It's a typical manner of responding to the question, "What effect does the predictor have on the likelihood of the event occurring?" [23]. The average marginal effect (AME) is a measure of the overall effect of the predictors that is used to assess the sorts of associations and magnitudes between explanatory variable levels and response probability levels [24, 25]. The means are just one of many sets of values that could be utilized, and none of them would have sounded problematic to a real person [26].
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