This research used cross-sectional data from the seven districts of the Assosa zone to classify determinants of married women's modern contraceptive to examining demographic and economic variables that had previously been considered in similar studies. The aim of this study was to examine both descriptive and inferential analysis in order to identify the determinants of married women's contraceptive practice status. As a result, 6866 married women were used in the report, and the findings are divided into two parts. The bivariate analysis (cross tabulation) is the first part of the result, which looked into the relationship between each explanatory variable and married woman’s contraceptive practices. As a result, most explanatory variables such as District, place of residence, women's age, husband education level, desire for child, husband occupation, women occupation, wealth index, women education level, religion, and knowledge of family planning, mass media exposure, number of living have a significant association with married women contraceptive practice at 5% level of significant(see table1). Similarly, the result in the table below also indicated that out of 6866 married women considered in the analysis 3121(45.46%) were practicing some form of contraceptive at the time of data collection. Utilization of modern contraceptive of married women has also varied significantly across Assosa zone districts.
Table 1. Cross tabulation of utilization of modern contraceptive and its determinants.
Variables name
|
category
|
Utilization of contraceptive
|
Total
|
Pearson chi-square
(p-value)
|
use
|
Not use
|
Count (%)
|
Count (%)
|
District
|
Bambasi
|
687(61.83)
|
424(38.16)
|
1111
|
831.718(0.000)
|
Homosha
|
389(36.1)
|
689(63.91)
|
1078
|
Menge
|
529(48.31)
|
566(51.69)
|
1095
|
Kurmuk
|
275(29.52)
|
795(70.47)
|
1070
|
Assosa
|
903(79)
|
240(20.99)
|
1143
|
Shilkole
|
232(29.63)
|
551(70.37)
|
783
|
Oda bildigilu
|
106(18.08)
|
480(81.9)
|
586
|
Total
|
3121(45.46)
|
3745(54.54)
|
6866
|
Place of resident
|
Urban
|
1844(83.36)
|
368(16.63)
|
2212
|
378.478(0.000)
|
Rural
|
678(14.57)
|
3976(85.43)
|
4654
|
Age of women
|
15-19
|
148(24.1)
|
467(75.93)
|
615
|
128.012(0.000)
|
20-24
|
476(34.34)
|
910(65.66)
|
1386
|
25-29
|
451(32.85)
|
922(67.15)
|
1373
|
30-34
|
292(29.79)
|
688(70.20)
|
980
|
35-39
|
268(26.38)
|
748(73.62)
|
1016
|
40-44
|
270(32.81)
|
553(67.19)
|
823
|
45-49
|
108(16.05)
|
565(83.95)
|
673
|
Education level of husband
|
No education
|
712(33.82)
|
1393(66.18)
|
2105
|
478.430(0.000)
|
primary
|
1833(66.92)
|
906(33.07)
|
2739
|
secondary
|
682(64.46)
|
376(35.54)
|
1058
|
higher
|
647(67.12)
|
317(32.88)
|
964
|
Desire for child
|
Not want
|
2138(73.93)
|
754(26.07)
|
2892
|
7.171(0.013)
|
Want
|
637(16.03)
|
3337(83.97)
|
3974
|
Number of living children
|
No child
|
301(31.72)
|
648(68.28)
|
949
|
346.308(0.00)
|
1_2 child
|
1075(51.98)
|
993(48.02)
|
2068
|
3_4 child
|
1551(73.51)
|
559(26.49)
|
2110
|
+5 child
|
1217(69.98)
|
522(30.02)
|
1739
|
Mass
media exposure
|
No
|
1472(29.94)
|
3445(70.06)
|
4917
|
682.651(0.000)
|
Yes
|
1209(62.03)
|
740(37.97)
|
1949
|
Husband occupation
|
Has work
|
4012(67.36)
|
1944(32.64)
|
5956
|
123.018(0.000)
|
Has no work
|
268(29.45)
|
642(70.55)
|
910
|
Women occupation
|
Has work
|
1548(69.10)
|
693(30.92)
|
2241
|
154.493(0.000)
|
Has no work
|
1382(29.88)
|
3243(70.12)
|
4625
|
Wealth index
|
Poor
|
723(24.52)
|
2225(75.47)
|
2948
|
762.237(0.000)
|
Middle
|
723(66.63)
|
362(33.37)
|
1085
|
Rich
|
2106(74.34)
|
727(25.66)
|
2833
|
Women Education status
|
No education
|
1283(32.24)
|
2697(67.76)
|
3980
|
469.309(0.000)
|
primary
|
1065(68.36)
|
493(31.64)
|
1558
|
secondary
|
642(78.10)
|
180(21.90)
|
822
|
higher
|
434(85.77)
|
72(14.23)
|
506
|
Knowledge family planning
|
No
|
9(4.26)
|
202(95.73)
|
211
|
176.508(0.000)
|
Yes
|
5452(81.92)
|
1403(21.08)
|
6655
|
Religion
|
Orthodox
|
1017(45.90)
|
1204(54.21)
|
2221
|
741.562(0.000)
|
Catholic
|
35(60.34)
|
23(39.65)
|
58
|
protestant
|
1089(63.46)
|
627(36.53)
|
1716
|
Muslim
|
731(17.62)
|
2018(82.38)
|
2749
|
Other
|
14(11.48)
|
108(88.52)
|
122
|
Figure 1 utilization of contraceptive among the seven districts of Assosa zone
As observed in the figure above, the highest percentage of contraceptive use was observed in Assosa district (79%), Bambasi (61.83%), Menge (48.31%). In contrast the least percentage of contraceptive practice was observed in Oda bildigilu (18.08%). The percentage of the rest variables can be observed in the table below (see figure1).
3.1.Test of heterogeneity proportions of modern contraceptive use.
The two-level structure is used with the district (Woreda) as the second-level unit and the married women as level one unit. This is based on the idea that there may be differences in married women’s contraceptive use between district that are not captured by the explanatory variables and hence may be regarded as unexplained variability within the set of all district (Snijders, 1999). Therefore, the Pearson chi-square for the proportion of utilization of modern contraceptive across the seven districts has been investigated in the table below Therefore; the Pearson chi-square for the proportion of utilization of modern contraceptive across the seven districts has been investigated in the table below. Consequently, the Pearson Chi-square, P-value =0.000 which is less than 0.05 level of significance, implying strong evidence of heterogeneity for married women contraceptive practice across districts(see Table 2).
Table 2 Chi-Square Tests of Heterogeneity
3.2.Model comparison of Bayesian and classical approach
Considering the standard errors of the calculated coefficients for comparison of both approaches (Bayesian and classical) is an effective process, and the model with the smaller standard error is the model that fits the data best. The result in the table below displays the approximate coefficients and standard errors for both approaches. Hence, the result indicated that all estimated coefficients’ standard errors in Bayesian random intercept model are smaller than the classical random intercept model (see table 3). Therefore, the Bayesian approach is better in fitting for this data than the classical approach.
Table 3 model comparison of Bayesian and classical models
Covariates
|
Category
estimated
|
BRIM
|
CRIM
|
SE
Comparison
|
Post.mean
|
S.EB
|
β
|
S.EC
|
Intercept
|
|
-3.643
|
0.0164
|
-3.2127
|
0.4408
|
S.EB< S.EC
|
Place
Of residence
|
Rural
|
-0.586
|
0.0032
|
-0.4720
|
0.0863
|
S.EB< S.EC
|
Women’s age
|
20-24
|
0.3777
|
0.0044
|
0.3068
|
0.1207
|
S.EB< S.EC
|
25-29
|
0.464
|
0.0043
|
0.3690
|
0.1221
|
S.EB< S.EC
|
30-34
|
0.37
|
0.036
|
0.2272
|
0.1250
|
S.EB< S.EC
|
35-39
|
0.1561
|
0.026
|
0.1305
|
0.1277
|
S.EB< S.EC
|
40-44
|
-0.1005
|
0.0014
|
-0.0787
|
0.1375
|
S.EB< S.EC
|
45-49
|
-0.9974
|
0.012
|
-0.8604
|
0.1609
|
S.EB< S.EC
|
Religion
|
Catholic
|
-0.0355
|
0.0120
|
-0.0100
|
0.3037
|
S.EB< S.EC
|
Protestant
|
-0.191
|
0.0042
|
-0.1515
|
0.0857
|
S.EB< S.EC
|
Muslim
|
-0.645
|
0.0013
|
-0.5243
|
0.129
|
S.EB< S.EC
|
Others
|
-1.43
|
0.034
|
-1.1850
|
0.3089
|
S.EB< S.EC
|
Husband education level
|
Primary
|
0.345
|
0.003
|
0.2831
|
0.154
|
S.EB< S.EC
|
secondary
|
0.087
|
0.006
|
0.0841
|
0.269
|
S.EB< S.EC
|
Higher
|
-0.0430
|
0.0041
|
-0.0338
|
0.1150
|
S.EB< S.EC
|
Desire for child
|
Want
|
0.0778
|
0.0021
|
0.0627
|
0.0590
|
S.EB< S.EC
|
Women education level
|
Primary
|
0.2238
|
0.0024
|
0.0091
|
0.0910
|
S.EB< S.EC
|
secondary
|
0.2861
|
0.0039
|
-0.0198
|
0.0959
|
S.EB< S.EC
|
Higher
|
0.4129
|
0.0051
|
-0.0494
|
0.0980
|
S.EB< S.EC
|
Number of living children
|
1_2
|
0.0118
|
0.0033
|
0.1854
|
0.0670
|
S.EB< S.EC
|
3_4
|
-0.0240
|
0.0034
|
0.2333
|
0.1071
|
S.EB< S.EC
|
>=5
|
-0.0537
|
0.0036
|
0.3401
|
0.1360
|
S.EB< S.EC
|
Women occupation
|
Has work
|
0.4291
|
0.0029
|
0.3649
|
0.0834
|
S.EB< S.EC
|
Husband’s work
|
Has work
|
0.1231
|
0.0019
|
0.1055
|
0.0533
|
S.EB< S.EC
|
Wealth index
|
Middle
|
0.6164
|
0.0028
|
0.5271
|
0.0780
|
S.EB< S.EC
|
Rich
|
0.9119
|
0.0026
|
0.7722
|
0.0714
|
S.EB< S.EC
|
Mass media exposure
|
YES
|
0.0152
|
0.0021
|
1.4716
|
0.3565
|
S.EB< S.EC
|
Knowledge of family planning
|
YES
|
1.6120
|
0.0128
|
0.0118
|
0.0559
|
S.EB< S.EC
|
Note: BRIM stands for Bayesian random intercept model, while CRIM stands for classical random intercept model.
As compared to women in urban areas, women in rural areas have a 42.9 percent lower probability of using contraception (odd ratio: 0.571, 95 percent credible interval (-0.763361 -0.367230). This may be due to the fact that family planning services are not distributed or accessible in rural areas as much as they are in urban areas. For women aged 20-24, the likelihood of using contraception is 43.5 percent (odd ratio: 1.435:95 percent) credible interval (0.0733, 0.6307) times higher than the reference age group of 15-49 years. It's also been discovered that women in the 25-29 age group are 55 percent more likely than women in the 15-19 age group to use contraception. When compared to orthodox women, married Muslim women had a 46.5 percent lower chance of using contraception. Married women with husbands with a primary education level are 0.386 (odd ratio: 1.38597 credible interval (0.176190, 0.479801) times more likely to use contraception than women with husbands with no education level (see Table 4).
Table 4 Posterior summaries for parameters of intercept model
Fixed Effects
|
Covariates
|
Categories
|
post.mean
|
S.d
|
Sd.error
|
2.5%
|
50%
|
97.5%
|
|
Intercept
|
-3.4330
|
0.5194
|
0.0164
|
-4.4915
|
-3.4175
|
-2.4220
|
Place of residence
|
Ref(urban)
|
---
|
---
|
---
|
---
|
---
|
---
|
Rural
|
-0.5611
|
0.1007
|
0.0032
|
-0.7634
|
-0.5605
|
-0.3672
|
Women’s Age
|
Ref(15-19)
|
---
|
---
|
--
|
---
|
--
|
---
|
20-24
|
0.3768
|
0.1386
|
0.0044
|
0.0733
|
0.3659
|
0.6307
|
25-29
|
0.4423
|
0.1363
|
0.0043
|
0.1694
|
0.4444
|
0.7047
|
30-34
|
0.2755
|
0.1454
|
0.0046
|
-0.0135
|
0.2776
|
0.5705
|
35-39
|
0.1561
|
0.1451
|
0.0046
|
-0.1237
|
0.1593
|
0.4359
|
40-44
|
-0.1005
|
0.1629
|
0.0052
|
-0.4212
|
-0.0942
|
0.2066
|
45-49
|
-0.9974
|
0.1856
|
0.0059
|
-1.3472
|
-0.9914
|
-0.6461
|
Religion
|
Ref(Ortho)
|
---
|
---
|
---
|
---
|
---
|
---
|
Catholic
|
-0.0355
|
0.3470
|
0.0110
|
-0.7105
|
-0.0305
|
0.6418
|
Protestant
|
-0.1852
|
0.1012
|
0.0032
|
-0.3834
|
-0.1886
|
0.0095
|
Muslim
|
-0.6247
|
0.0845
|
0.0027
|
-0.7938
|
-0.6223
|
-0.4552
|
Others
|
-1.4105
|
0.3497
|
0.0111
|
-2.0858
|
-1.4128
|
-0.7361
|
Husband’s education
|
Ref(No.)
|
---
|
---
|
---
|
---
|
---
|
---
|
Primary
|
0.3264
|
0.0753
|
0.0024
|
0.1762
|
0.3294
|
0.4798
|
Secondary
|
0.0891
|
0.1109
|
0.0035
|
-0.1328
|
0.088
|
0.3092
|
Higher
|
-0.0430
|
0.1282
|
0.0041
|
-0.3089
|
-0.0467
|
0.2142
|
Desire for children
|
Ref (not want)
|
---
|
---
|
---
|
---
|
---
|
---
|
Want
|
0.0778
|
0.067
|
0.0021
|
-0.0595
|
0.0798
|
0.2072
|
Women’s education
|
Ref(No )
|
---
|
---
|
---
|
---
|
---
|
---
|
Primary
|
0.2238
|
0.0771
|
0.0024
|
0.0738
|
0.2230
|
0.3763
|
Secondary
|
0.2861
|
0.1232
|
0.0039
|
0.0458
|
0.2897
|
0.5261
|
Higher
|
0.4129
|
0.1604
|
0.0051
|
0.1209
|
0.4139
|
0.7192
|
Number living
children
|
Ref (No)
|
---
|
---
|
---
|
---
|
---
|
---
|
1_2
|
0.0118
|
0.1037
|
0.0033
|
-0.1829
|
0.0108
|
0.2239
|
3_4
|
-0.0240
|
0.1083
|
0.0034
|
-0.2293
|
-0.0232
|
0.1860
|
>=5
|
-0.0537
|
0.1122
|
0.0036
|
-0.2691
|
-0.0565
|
0.1838
|
Women occupation
|
Ref ( no work)
|
---
|
---
|
---
|
---
|
---
|
---
|
Has work
|
0.4291
|
0.0924
|
0.0029
|
0.2442
|
0.4322
|
0.6135
|
Husband’s work status
|
Ref( no work)
|
---
|
---
|
---
|
---
|
---
|
---
|
Has work
|
0.1231
|
0.0606
|
0.0019
|
0.0017
|
0.1231
|
0.2388
|
Wealth index
|
Ref (poor)
|
---
|
---
|
---
|
---
|
---
|
---
|
Middle
|
0.6164
|
0.0897
|
0.0028
|
0.4342
|
0.6180
|
0.7823
|
Rich
|
0.9119
|
0.0825
|
0.0026
|
0.7463
|
0.9115
|
1.0746
|
Mass media
|
Ref (No)
|
---
|
---
|
---
|
---
|
---
|
---
|
Yes
|
0.0152
|
0.0652
|
0.0021
|
-0.1127
|
0.0146
|
0.1410
|
Knowledge of
Family planning
|
Ref(No)
|
---
|
---
|
---
|
---
|
---
|
---
|
Yes
|
1.6120
|
0.4052
|
0.0128
|
0.8545
|
1.5971
|
2.4533
|
Random effect
|
var
|
|
0.613
|
0.3926
|
0.0124
|
0.2856
|
0.6433
|
1.7660
|
3.3 Intra class correlation
The intra woreda correlation coefficient for this study was estimated Where 3.29 is the logistic distribution variance. This indicated that about 15.71% of the total variability in married women contraceptive practice is due to the fact that differences across regions and the remaining unexplained 84.29% accounts the between married women differences.
3.4 Checking Convergence
This is the graph that shows the number of iterations versus the generated values. If all of the values in this graph are found within an area with no strong periodicities, convergence can be achieved (up and down periods). As a consequence, none of the trace plots below have any up and down times. Furthermore, density plots are almost identical to normal plots. This means that all posterior figures have been converged (see figure 2).
3.5.Discussions of the results
On the basis of their respective standard errors, the models chosen in the Bayesian framework were compared to the classical model. As a result, the Bayesian model was found to be superior to the classical model since the standard errors of predictors in this model were lower. The Bayesian random intercept model has shown that the random intercept is significantly different from zero in this model, suggesting that contraceptive practice among married women differs by woreda. This analysis appears to be in agreement with previous research (Hailu, 2015).
The location of a woman's residence has a direct impact on her contraceptive use. As a result, women who live in rural areas are less likely to use contraception than women who live in urban areas. This finding is consistent with previous studies (Balew,2015; Worku, 2015). Similarly, the study found that women of Muslim and other religions have a detrimental effect on contraceptive use, which seems to agree with the findings of a study conducted in Ethiopia (Hailu, 2015; Tiruneh, 2015).
According to the findings, a woman's occupation is a major determinant of her contraceptive use status. As a result, working women were more likely than non-employed women to use contraception. This logic may have arisen as a result of the fact that working women are more likely to have access to contraception than their counterparts due to improved economic conditions. This finding appears to be consistent with a previous analysis of a similar scenario (Endriyas, 2017).
With the MCMCglmm packages in R program, the posterior inference was implemented with the Metropolis-Hasting algorithm with 60000 iterations, 10,000 samples as burn in, and 50 thinning interval to render the sequence sampling independent or low autocorrelation.