Socio-demographic characteristics of study participants
A total of 7589 respondents were included in the study. More than one fourth 2,165 (28.5%) of the respondents were in the age range of 25–29. Of all the respondents, 662 (87.2%) were rural dwellers. Regarding to region, about 3129 (41.2%) were Oromia whereas 33 (0.4%) were Dire Dawa. Among the total 1654(21.8%) were in the poorer wealth quantile category and 4799(63.2) were lowered community education. More than half, 4406(58%) of the respondents had a big problem to reach health facility whereas only 2414 (32%) had ANC visit more than 4 + times. According to media exposure, 2564(33.8%) the study participants had media exposure [Table 1].
Table 1
Percentage distribution of characteristics of respondents in 2016 Ethiopian Demographic and Health Surveys.
Characteristics | Weighted frequency | Percentage |
Age | | |
15–19 | 339 | 4.5 |
20–24 | 1,465 | 19.3 |
25–29 | 2,165 | 28.5 |
30–34 | 1,661 | 22 |
35–39 | 1,206 | 16 |
40–44 | 546 | 7 |
45–49 | 207 | 2.7 |
Residence | | |
Rural | 6620 | 87.2 |
Urban | 969 | 12.8 |
Religion | | |
Orthodox | 2,882 | 38 |
Protestant | 1,651 | 21.8 |
Muslim | 2,824 | 37.2 |
Others* | 232 | 3 |
Marital status | | |
Single | 144 | 2 |
Married | 7020 | 92.5 |
Widowed | 95 | 1.2 |
Divorced | 233 | 3 |
Separated | 97 | 1.3 |
Region | | |
Somali | 269 | 3.5 |
Tigray | 537 | 7.1 |
Afar | 71 | 1 |
Amhara | 1,632 | 21.5 |
Oromia | 3,129 | 41.2 |
Benishangul-Gumuz | 81 | 1.1 |
SNNPR | 1,600 | 21.1 |
Gambelia | 21 | 0.3 |
Harari | 17.4 | 0.2 |
Addis Ababa | 198.3 | 2.6 |
Dire dawa | 33.3 | 0.4 |
Wealth index | | |
Poorest | 1,651 | 21.7 |
Poorer | 1,654 | 21.8 |
Middle | 1,588 | 21 |
Richer | 1,427 | 18.8 |
Richest | 1,269 | 16.7 |
Community women’s education | | |
Lower community education | 4799 | 63.2 |
Higher community education | 2790 | 36.8 |
Occupational status | | |
Not working | 4,078 | 53.7 |
Working | 3,511 | 46.3 |
Distance to health facility | | |
A big problem | 4,406 | 58 |
Not a big problem | 3,183 | 42 |
Parity | | |
0–4 | 4,624 | 61 |
5–9 | 2,732 | 36 |
10+ | 233 | 3 |
Family size | | |
1–4 | 2331 | 31 |
5–9 | 4,875 | 64 |
10+ | 383 | 5 |
ANC visit | | |
0–3 times | 5,175 | 68 |
4 + times | 2,414 | 32 |
Media exposure | | |
Not have media exposure | 5025 | 66.2 |
Have media exposure | 2,564 | 33.8 |
*key: others- traditional, catholic |
Regional Prevalence Of Iron Supplementation Among Pregnant Women
The prevalence of iron supplementation among pregnant women varies across the country. The highest and lowest prevalence of Iron supplementation among pregnant women was observed in Tigray (77.2%) and in Somali (27.7%) regions respectively [Figure 1].
Spatial Analysis
Spatial analysis of Iron supplementation among pregnant women
A total of 622 clusters were included in the spatial analysis of iron supplementation. Each point on the map characterizes one enumeration area with the proportion of iron supplementation in each cluster. The yellow color indicates areas with a high proportion of iron supplementation whereas the red color indicates enumeration areas with a low proportion of iron supplementation. The higher proportion of iron supplementation has occurred in a majority of the Tigray region, Northeast part of Amhara, west part of Beneshangul Gumuz, North east part of SNNPR and the entire part of Addis Ababa. Whereas the low proportion of iron supplementation was accumulated in Somali, Afar, South west part of Oromia, West part of Gambella and South west part of Addis Ababa [Figure 2].
Spatial Autocorrelation
The spatial distribution of iron supplementation among pregnant women was found to be non-random in Ethiopia with Global Moran’s 0.30 (p < 0.001).The clustered patterns (on the right sides) show high rates of iron supplementation occurred over the study area. Given the z-score of 10.6 indicated that there is less than 1.5% likelihood that this clustered pattern could be the result of chance. The bright red and blue colors to the end tails indicate an increased significance level [Figure 3].
Cluster and outlier analysis of iron supplementation among pregnant women
Cluster and outlier analysis was conducted to identify the nature of clustering by using local Moran’s I. The red color (low- low cluster) indicates that iron supplementation hot spot areas means low rate of iron supplementation surrounded by low rate of iron supplementation, the green (high-high cluster) color indicates iron supplementation cold spot areas mean high rate of iron supplementation surrounded by high rate of iron supplementation and the pink (High outlier) means high rate of iron supplementation surrounded by low rate of iron supplementation and dark yellow (Low outlier) colors indicates low rate of iron supplementation surrounded by high rate of iron supplementation. Significant clusters were found in Tigray, Amhara, Gambela, SNNPR, Oromia, Somali, Addis Ababa, and Drie Dawa. Hot spot areas for iron supplementation were found in south east Somali, Northwest Gambela, and south west Somali, North west SNNPR, East Afar While the cold spot regions were found in Northwest Amhara, East part of Addis Ababa, and Northeast Gambella. Outliers were found in the central and southern parts of Amhara, south east Afar, Benshangul Gumuz, and central Somali regions, Dire Dawa, Hareri, east Oromia, east part of Addis Ababa and North East Part of SNNPR [Figure 4 ].
Hot Spot Analysis Of Iron Supplementation Among Pregnant Women
The red color indicates that significant hot spot areas (low iron supplementation) and found in Northeast Somali, South Afar, North West Gambela, West and east part of SNNPR and Southwest Oromia regions (P < 0.01). Whereas the yellow color indicates those significant more on non-risk areas (Cold spot areas) found in Tigray, North part of Amhara, East part of Addis Ababa, North West hareri regions (Fig. 5).
Interpolation of Iron Supplementation Among Pregnant Women
North West Gambela, east Somali, southwest Somali, North West Oromia, Northeast Afar were identified as predicted more risky areas of iron supplementation as compared to other regions. Whereas Tigray, North West Amhara, Northeast Addis Ababa, West Beneshangul Gumuz, Northeast Addis Ababa and North SNNPR were found predicted low-risk areas [Figure 6].
Spatial Sat Scan analysis of iron supplementation (Bernoulli based model).
Spatial scan statistics were done using SaTScanv9.6 to identify most likely clusters, and a total of 10 significant clusters with 271 enumeration areas were identified. Among the total, 1 of them was most likely (primary) clusters and 9 were secondary clusters. The primary clusters spatial window red color was located in southwest Somali and central part of Oromia region which was centered at 5.330795 N, 41.837597 E of geographic location with 441.87 km radius, and Log-Likelihood ratio (LLR) of 66.68, at p < 0.001 which was detected as the most likely cluster with maximum LLR. It showed that pregnant women within the spatial window had 1.35 times higher risk of low iron supplementation than pregnant women outside the window. The other secondary clusters were described as detail in the table [Table 2] [Figure 7].
Table 2
Sat Scan analysis of iron supplementation use among pregnant women in Ethiopia, 2016
Cluster | Enumeration area(cluster)identified | Coordinate/radius | Population | Case | RR | LLR | p-value |
1(89) | 556, 394, 480, 187, 520, 318, 278, 208, 164, 358, 377, 85, 289, 286, 472, 138, 452, 7, 492, 422, 543, 92, 490, 198, 171, 95, 34, 146, 82, 497, 518, 123, 405, 562, 521, 588, 553, 26, 468, 316, 458, 601, 213, 398, 319, 576, 313, 619, 529, 365, 600, 21, 245, 445, 232, 589, 12, 214, 372, 634, 251, 32, 182, 573, 476, 391, 574, 524, 239, 122, 308,216, 578, 215, 116, 22, 408, 148, 438, 522, 412, 513, 454, 506, 580, 68, 115, 133, 501, 453, 607, 568 | (5.330795 N, 41.837597 E) / 441.87 km | 1240 | 852 | 1.35 | 66.68 | < 0.001 |
2 (37) | 377, 394, 422, 7, 34, 289, 480, 398, 316, 601, 82, 556, 405, 21, 518, 468, 232, 472, 600, 208, 313, 182, 445, 574, 32, 576, 286, 634, 26,365, 452, 520, 12, 215, 216, 408 | (5.203234 N, 40.019732 E) / 261.38 km | 510 | 391 | 1.47 | 60.01 | < 0.001 |
3(12) | 66, 618, 309, 435, 536, 370, 507, 592, 104, 260, 233, 69 | (8.389747 N, 33.258557 E) / 71.61 km | 146 | 132 | 1.7 | 46.72 | < 0.001 |
4(49) | 630, 378, 269, 629, 77, 146, 92, 490, 543, 171, 492, 198, 95, 497, 458, 588, 553, 521, 138, 214, 33, 573, 251, 239, 116, 85, 358, 22,164, 527, 568, 277, 439, 64, 57, 278, 210, 8, 186, 566, 1, 318, 622,436, 212, 454, 501 | 7.717178 N, 46.991580 E) / 555.85 km | 587 | 423 | 1.37 | 43.43 | < 0.001 |
5(10) | 1, 566, 622, 186, 307, 436, 212, 8, 210, 419 | (9.505470 N, 42.438628 E) / 33.79 km | 142 | 117 | 1.54 | 25.76 | < 0.001 |
6(42) | 477, 325, 207, 437, 376, 154, 168, 177, 552, 459, 371, 243, 465, 299, 526, 554, 197, 46, 586, 489, 119, 338, 76, 326, 555, 470, 337, 432,486, 447, 448, 62, 306, 227, 446, 411, 219, 558, 270, 593, 265, 406 | (7.173968 N, 35.802680 E) / 170.61 km | 509 | 343 | 1.27 | 20.07 | < 0.001 |
7(7) | 372, 93, 412, 333, 476, 506, 453 | (8.949350 N, 41.312402 E) / 65.76 km | 105 | 85 | 1.51 | 16.91 | < 0.001 |
8(2) | 544, 599 | (12.349981 N, 40.242399 E) / 29.62 km | 34 | 33 | 1.8 | 16.61 | < 0.001 |
9(16) | 150, 36, 183, 559, 184, 246, 533, 244, 137, 364, 35, 498, 615, 320, 515, 494 | (10.512406 N, 36.129050 E) / 76.33 km | 198 | 139 | 1.31 | 11.04 | 0.011 |
10(7) | 20, 276, 283, 547, 102, 37, 55 | (10.381987 N, 40.265796 E) / 75.25 km | 91 | 70 | 1.43 | 10.33 | 0.019 |
Multilevel Logistic Regression Analysis
Random effect analysis results
The intra class correlation in the null model indicated that 27% of the total variability for iron supplementation was due to differences between clusters/EA, with the UN explained 67% attributable to individual differences.
Furthermore, the median odds ratio revealed that iron supplementation among pregnant women was heterogeneous. The median odds ratio for iron supplementation was 2.9 in the empty model which indicates that there was variation between clusters. If we randomly select pregnant women from two different clusters women at the cluster with favorable iron supplementation had 2.9 m times higher odds of experiencing iron supplementation as compared with pregnant women at cluster with lower favorable with iron supplementation.
About 67% of the variability in iron supplementation was explained by the full model (pcv = 67%) [Table 3]. Therefore, the two-level multilevel logistic regression model was used to get an unbiased standard error and to make a valid inference.
Table 3
Multivariable multilevel logistic regression analysis result of both individual and community level factors associated with iron supplementation in pregnant women Ethiopia, EDHS 2016.
Community and individual level variables | Null model | Model II AOR(95%CI) | Model III AOR(95%CI) | Model IV AOR(95%CI) |
Age | | | | |
15–19 | | 1.27 [0.78–2.06] | | 1.38 [0.85–2.23] |
20–24 | | 1.34 [0.87–2.06] | | 1.41 [0.92–2.16] |
25–29 | | 1.21 [0.80–1.83] | | 1.29 [0.85–1.95] |
30–34 | | 1.01 [0.67–1.52] | | 1.06 [0.71–1.59] |
35–39 | | 0.88 [0.59–1.33] | | 0.89 [0.59–1.33] |
40–44 | | 0.71 [0.46–1.09] | | 0.70 [0.45–1.07] |
45–49 | | 1 | | 1 |
Residence | | | | |
Rural | | | 1 | |
Urban | | | 1.81 [1.42–2.33] | 1.24 [0.92–1.66] |
Region | | | | |
Somali | | | 1 | 1 |
Tigray | | | 9.18 [6.35–13.28] | 5.35 [3.73–7.69]** |
Afar | | | 1.75 [1.22–2.51] | 1.61 [1.13–2.28]** |
Amhara | | | 3.08 [2.19–4.33] | 2.07 [1.47–2.91]** |
Oromia | | | 1.25 [0.89–1.75] | 0.81 [0.58–1.14] |
Benishangul-Gumuz | | | 2.37 [1.62–3.46] | 1.37 [0.94–1.99] |
SNNPR | | | 1.91 [1.35–2.71] | 1.13 [0.80–1.59]. |
Gambelia | | | 1.02 [0.68–1.52] | 0.76 [0.52–1.13] |
Harari | | | 2.10 [1.39–3.16] | 1.47 [0.98–2.19] |
Addis Ababa | | | 1.92 [1.24–2.96] | 0.90 [0.58–1.38] |
Dire dawa | | | 2.95 [1.94–4.48] | 1.54 [1.03–2.33]* |
Wealth index | | | | |
Poorest | | 1 | | 1 |
Poorer | | 1.45 [1.21–174] | | 1..33 [1.15–1.68]** |
Middle | | 1.40 [1.15–1.71] | | 1.39 [1.10–1.63]** |
Richer | | 1.49 [1.21–1.84] | | 1.42 [1.13–1.76]** |
Richest | | 1.58 [1.26–1.98] | | 1.18 [0.89–1.57] |
Community women’s education | | | | |
Lower community education | | | 1 | 1 |
Higher community education | | | 1.61 [1.32–1.97] | 1.31 [1.07–1.59]** |
Occupational status | | | | |
Not working | | 1 | | 1 |
Working | | 1.20 [1.06–1.35] | | 1.12 [0.99–1.26] |
Distance to health facility | | | | |
A big problem | | | 1 | 1 |
Not a big problem | | | 1.43 [1.26–1.61] | 1.32 [1.16–1.50]** |
Parity | | | | |
0–4 | | 1 | | 1 |
5–9 | | 1.04 [0.88–1.23] | | 1.07 [0.91–1.26] |
10+ | | 1.05 [0.70–1.56] | | 1.10 [0.74–1.65] |
Family size | | | | |
1–4 | | 1 | | 1 |
5–9 | | 0.98 [0.85–1.13] | | 1.01 [0.87–1.16] |
10+ | | 0.97 [0.73–1.29] | | 1.06 [0.80–1.41] |
ANC visit | | | | |
0–3 times | | 1 | | 1 |
4 + times | | 3.9 [3.43–4.44] | | 3.66 [3.21–417]** |
Media exposure | | | | |
Not have exposure | | 1 | | 1 |
Have exposure | | 1.39 [1.20–1.61] | | 1.33 [1.15–1.53]** |
Constant | 0.88 [0.80–98] | 0.28 [0.18–0.44] | 0.24 [0.18–0.31] | 0.16 [0.10–0.27] |
Model comparison and random effects | | | | |
ICC | 0.27 [0.24-31] | | | |
Log likelihood (LL) | -4552.55 | -4213.56 | -4373.57 | -4113.25 |
Deviance | 9105.1 | 8427.12 | 8747.14 | 8226.5 |
PCV | Ref | 0.41 | 0.59 | 0.67 |
MOR | 2.9(2.65,3.2) | 2.27(2.09,2.49) | 1.97(1.84,2.5) | 1.84(1.70,2.00) |
Key: AOR: Adjusted odds ratio; CI: confidence interval; ICC: intra-cluster correlation; MOR: median odds ratio; 1: reference group; p-value 0.05 − 0.01 *: p-value < 0.01 **: ANC; antenatal care visit |
The Fixed Effect Analysis Result
Bi-variable multilevel logistic regression analysis was done to identify variables for multivariable multilevel logistic analysis and Variable with a p-value less than 0.2 were considered for multivariable analysis.
The combined multilevel logistic regression model (model 4) was the best-fitted model for this data because this model had high likelihood and low deviance and in addition LR test vs. logistic model: chibar2 (01) = 141.94, Prob > = chibar2 = 0.0000 the likelihood ratio test indicates that multilevel logistic regression with individual and community level factors was the best-fitted model to handle the data.
In multivariable multilevel logistic regression analysis individual-level factors such as wealth index ANC visit, and media exposure were found to be significantly associated with the odds of iron supplementation. Whereas, in community factors region, community education and distance to health facilities were significantly associated with iron supplementation.
With adjusting other covariates, women’s in Tigray, Afar, Amhara and Dire Dawa regions were 5.35 (AOR = 5.35, 95%CI: 3.73, 7.69), 1.61 (AOR = 1.61, 95%CI: 1.13, 2.28), 2.07 (AOR = 2.07, 95%CI: 1.47, 2.91) and 1.54 (AOR = 1.54, 95%CI: 1.03, 2.33) times higher iron supplementation use than that of women’s in Somali region, respectively.
Based on WHO recommendation, women who attended the minimum four ANC visit 3.66 times (AOR = 3.66, 95%CI: 3.21, 417) more likely to take the iron tablet as compared to those who didn't attend minimum requirement ANC visit.
Those mothers who were in poorer, middle and richer wealth quantile categories were 1.33 (AOR = 1.33, 95%CI: 1.15, 1.68), 1.39 (AOR = 1.39, 95%CI: 1.10, 1.63) and 1.42 (AOR = 1.13, 95%CI: 1.131.76) times higher iron supplementation use than that of women’s in poorer wealth quantile.
Community educational level of women was significantly associated with iron supplementation use. Women having higher community education 1.31 times [AOR = 1.31, 95%CI, 1.07, 1.59) more likely to take iron supplementation as compared to those lower community education.
The odds of iron tablet use was 1.32 times (AOR = 1.32, 95%CI: 1.16, 1.50) higher among women who hadn't a problem to the distance of health facility as compared to their counterparts.
The odds of iron taking during pregnancy were 1.33 times (AOR = 1.33, 95%CI: 1.15, 1.53) higher among those who had media exposure as compared to their counterparts [Table 3].