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Anaemia has prevailed as a mild to severe public health problem in Ethiopian women of reproductive age. Many studies carried out on anaemia have been limited to subnational assessments and subgroups of women. The effects of potential factors thought to affect anaemia and severity levels of anaemia have not been well considered. Therefore, this study identifies individual, household and community level factors associated with anaemia among women of reproductive age in Ethiopia applying multilevel ordinal logistic regression models. Proportional odds assumption was tested by likelihood ratio test. About 35.6% of the variation on anaemia was due to between household and community level differences. Pregnancy (adjusted odds ratio [AOR] = 2.30, 95% confidence interval [CI]: 1.82, 2.91), HIV (AOR = 2.40, 95% CI: 1.76, 3.25), giving birth once (AOR = 1.2, 95% CI: 1.05, 1.40), giving birth more than once (AOR = 1.4, 95% CI: 1.19, 1.71), living with five or more family members (AOR = 1.24, 95% CI: 1.05, 1.47), living in poorest households (AOR = 1.34, 95% CI: 1.2, 1.61) and rural area (AOR = 1.57, 95% CI: 1.28, 1.92) were associated with greater odds of more severe anaemia compared with their respective counter parts. Secondary and above education (AOR = 0.83, 95% CI: 0.70, 0.97) and use of pills, implants or injectable (AOR = 0.67, 95% CI: 0.59, 0.77) were associated with lower odds of more severe anaemia. Anaemia prevention and control programmes need to be strengthened for women living with HIV/AIDS and during pregnancy. Household poverty reduction and social protection services need to be strengthened and integrated in anaemia prevention and management activities in women.
This study used 2016 Ethiopia Demographic and Health Survey (EDHS) data set collected from the nine regions and two administrative cities of Ethiopia. The 2016 EDHS is the fourth and the most recent nationally representative survey conducted with the main objective of providing timely and reliable data on health and demographic outcomes (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Stratified two‐stage sampling technique was used to select enumeration areas (EAs) and households. An EA is a geographic area covering on average 181 households (HHs). The 2007 Ethiopia Population and Housing Census (PHC) was used as a sampling frame to select EAs. In the first stage, 645 EAs (202 in urban and 443 in rural) were selected with probability proportional to EA size. The EA size is the number of residential households in the EA as determined in the 2007 PHC. And those EAs with more households have higher probability of being selected. In the second stage, 18,008 HHs were selected by systematic sampling technique, with average of 28 HHs per EAs. All WRA in the selected HH were eligible for anaemia testing (Figure 1; Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Sample size and sampling procedure for factors associated with anaemia among women of reproductive age in Ethiopia, 2018: Data from 2016 Ethiopia Demographic and Health Survey (EDHS) After obtaining permission from the Inner City Fund (ICF) International; individual, household and HIV data sets were downloaded from the DHS website (http://dhsprogram.com). Details on sampling technique, sample size, data collection tools, data quality control and ethical concerns are available in 2016 EDHS report (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). All WRA who had data on anaemia status were included in this study. A total of 14,489 WRA who were tested for anaemia were included in the analysis. Anaemia is an ordered categorical variable categorized as none, mild, moderate and severe anaemia based on Hb level. Blood samples were taken from a finger prick of the voluntarily consented women and collected in a micro cuvette. Hb analysis was carried out on‐site using a battery‐operated portable HemoCue analyser (Central Statistical Agency [CSA; Ethiopia] and ICF, 2016). Hb levels were adjusted for pregnancy because during pregnancy the increase in maternal blood volume and the iron needs of the fetus decrease the blood Hb level (World Health Organization, 1989). It was also adjusted for smoking and altitude. The World Health Organization (WHO) Hb cut off points for diagnosis of anaemia are given in Table 1 (World Health Organization, 2001). The WHO Hb cut off points for diagnosis of anaemia Abbreviations: g/dl, gram/deciliter; Hb, haemoglobin; NP‐NL, neither pregnant nor lactating; WHO, World Health Organization. After reviewing recent literature, potential risk factors of anaemia were extracted from the data set. Due to the hierarchical nature of the 2016 EDHS data, the extracted variables were classified as individual, household and community level variables. Individual level variables were characteristics of the women which were specific to each woman (Table 2). Individual level variables extracted from EDHS 2016 data set for studying factors associated with anaemia 0. 15–24 1. 25–34 2. 35–49 0. No formal education 1. Primary 2. Secondary and above 0. Protestant 1. Orthodox 2. Muslim 3. Other 0. Not living with husband 1. Living with husband 0. Yes 1. No 0. None 1. Less than once/week 2. At least once/week 3. More than once/week 0. Yes 1. No 0. Pregnant 1. Lactating 2. Neither pregnant nor lactating 0. Yes 1. No 0. None 1. Pill/injectables/implants 0. IUD 1. Nonhormonal 0. No 1. One child 2. More than one children 0. Yes 1. No 0. Yes 1. No 0. Yes 1. No 0. Negative 1. Positive Abbreviation: EDHS, Ethiopia Demographic and Health Survey; IUD, intrauterine device. Household level variables are household level characteristics which are common for all women living in the same household and include variables described in Table 3. Household level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Poorest 1. Poor 2. Middle 3. Rich 4. Richest 0. ≤2 1. 3 and 4 2. ≥5 persons 0. Cleaner fuel 1. Solid fuel 0. Improved 1. Unimproved 0. Improved sources 1. Unimproved sources Community level variables were characteristics which are common for all women residing in the same community (cluster) and include place of residence, region, community (cluster) women education, community poverty, community women unemployment and community mass media exposure. Variables like community women education, community poverty, community women unemployment and community mass media exposure were generated by aggregating individual characteristics within the cluster. The generated variables were further categorized as low or high based on the national median values of the generated variables. These variables are measured as shown in Table 4. Community level variables extracted from Ethiopia Demographic and Health Survey 2016 data set for studying factors associated with anaemia 0. Urban 1. Rural 0. Tigray 1. Afar 2. Amhara 3. Oromia 4. Somali 5. Benishangul Gumuz 6. SNNPR 7. Gambela 8. Harari 9. Addis Ababa 10. Dire Dawa 0. Low 1. High 0. Low 1. High 0. High 1. Low 0. High 1. Low Due to hierarchical nature of the 2016 EDHS data where individuals are nested within households and households are in turn nested within clusters, multilevel (three‐level) OLR was used. Ignoring hierarchical nature and use of single‐level analysis could result in biased estimation of parameters and standard errors. Furthermore, the assumption of independent observation in ordinary logistic regression does not hold true in hierarchical data. Multilevel analysis handles these limitations by examining simultaneously the effects of explanatory variables at different levels (Diez‐Roux, 2000). OLR is a well‐suited technique to this study because of the ordered nature of outcome variable (none, mild, moderate and severe anaemia; Hedeker, 2015). Stata software version 14 was used for analysis of the data. A P‐value ≤ 0.25 in bivariate analysis was used to consider candidate variables for multivariable analysis (Stoltzfus, 2011). In a multivariable analysis, a P value < 0.05 was used to identify variables significantly associated with anaemia. Adjusted odds ratios with 95% confidence intervals were estimated and interpreted (Raman & Hedeker, 2005). The proportion of variations in odds of anaemia between households and communities was expressed using variance partition coefficients (VPC). The VPC measures the proportion of outcome (anaemia) variation unexplained by the predictor variables that lies at each level of the model hierarchy. It measures the relative importance of clusters, households and individual (women) as sources of variation on anaemia status (Leckie & French, 2013). The mixed‐effects OLR (proportional odds) model can be written in terms of the cumulative logits as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ij + u i) P ijkc—is accumulative probability of being at ‘c’ category of anaemia for kth individual in jth household and ith cluster. ᵧc—is a model threshold or intercept for C‐1 level of anaemia, and it is a fixed parameter. It represents the cumulative logits of being at or below C‐1 level of anaemia when the covariates and random effects equal to zero. It is strictly increasing (i.e., γ1 < γ2 < · · · < γC − 1). C = number of categories of anaemia which equals to 4. β—is a coefficient (fixed effect of explanatory variable). X ijk—is a covariate vector for kth individual in jth household and ith cluster. u ij—is level‐2 (household) random effect, and it is assumed to be normally distributed with variance σ2(v2). ui—is level‐3 (cluster) random effect, and it is assumed to be normally distributed with variance σ2(v3). (Raman & Hedeker, 2005). Violation of the proportional odds assumption is common. In such occasions, a model which relaxes the assumption is nonproportional or partial‐proportional odds model in which covariates are allowed to have different effects on the C − 1 cumulative logits. It is given as below in the box: Log Pijkc1−Pijkc = ᵧc − (x ijk β + u ijk α c + u ij + u i) u ijk—is a covariate vector for set of variables for which proportional odds is not assumed. α c—is a vector of regression coefficients associated with these covariates for C‐1 levels of outcome. Because α c carries the c subscript, the effects of these covariates are allowed to vary across the C − 1 cumulative logits. (Raman & Hedeker, 2005) Both household and cluster random effects variance was expressed in terms of VPC. VPC(3) is a proportion of total variation on anaemia attributable to cluster random effect. It is given as in the box: VPC(3) = σ2v3σ2v3+σ2v2+π2/3, where π2/3 is individual level variance which equals to 3.29. σ2(ν3)—is cluster (level‐3) random effect variance. σ2(ν2)—is household (level‐2) random effect variance. VPC for level‐2 and 3 clustering effects (VPC(2 + 3)) is a proportion of total variation on anaemia attributable to both household and cluster level random effect. It is given as below (Leckie & French, 2013). VPC(2 + 3) = σ2v2+σ2v3σ2v3+σ2v2+π2/3.VPC(2) is a proportion of total variation on anaemia attributable to household level random effect. It is given as: VPC(2) = σ2v2σ2v3+σ2v2+π2/3. The explained variances at cluster and household level were quantified by proportional change in variance (PCV; Merlo, Yang, Chaix, Lynch, & RÅstam, 2005). The proportional odds assumption states that the effects of all covariates are constant across categories of outcome variable. After fitting both proportional and nonproportional odds models, the proportional odds assumption was tested using likelihood ratio test. It tests the null hypothesis that there is no difference in the effects of explanatory variables across the levels of anaemia. The P value ≥ 0.05 is desirable to retain null hypothesis (Bauer & Sterba, 2011). The likelihood ratio test supported the nonproportional odds assumption. Furthermore, each variable in the model was tested to identify the variables for which the proportional odds assumption was violated. An Akaike information criterion (AIC) was used to select the final model which fits the data best compared with other fitted models. The AIC of the all models were compared, and the model with the lowest AIC was considered as the best model fits the data (Hox, Moerbeek, & Van De Schoot, 2010; Table 6 ). Random intercept variances and model fit statistics of three‐level mixed effect models Note: σ2(ν3) and σ2(ν2) are community and household random intercept variances, respectively. VPC(3), variance partition coefficient for cluster, VPC(2 + 3), variance partition coefficient for household and cluster, VPC(2), variance partition coefficient for household. PCV3, proportional change in cluster level variance; Model 1, model with no independent variable; Model 2, model adjusted for individual level variables; Model 3, model adjusted for household level variables; Model 4, model adjusted for community level variables; Model 5, model adjusted for individual, household and community level variables simultaneously. Abbreviation: AIC, Akaike information criteria. For a substantial number of clusters and households, the 95% confidence intervals of random intercepts do not overlap zero. This implied that the random effects of the many households and clusters on anaemia were significantly different from zero (above or below zero; Leckie & French, 2013). The normality assumptions were tested graphically using quantile‐quantile plots (Leckie & French, 2013). The result suggested that cluster and household random effects were approximately normally distributed, respectively. This implied that the final model is appropriate for predicting the outcome variable and describing the data at hand (adequate). The permission for access to the data was obtained from ICF International by registering and stating the objective of the study. The data set has no individual names or house hold addresses. The data were used for the registered research topic only and were not shared to another person.
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