Family planning coverage has improved in Ethiopia in the last decade, though fertility is still about 5.8 in the rural setup. In this paper, the major structural determinants of family planning service were analyzed using a multilevel model from 8906 individual women observation in the 2011 EDHS data. The results show that there is a big variation in family planning use both at the individual and between group levels. More than 39% of the variation in FP use is explained by contextual cluster level differences. Most of the socioeconomic predictors; respondent’s education, ethnicity, and partners’ education as well as employment status and urbanization were found to be significant factors that affect FP use. Similarly health extension visit and media access were found to be strong factors that affect FP service at both individual and cluster levels. This evidence concludes that addressing these contextual factors is very crucial to strengthen FP use and fertility reduction in the nation, beyond individual behavioral changes.
Since Malthus, and specifically since the 1994 Cairo conference, high fertility and fast population growth are perceived as one of the deterring factors for development [
Conceptually FP programs are believed to have a wider benefit than the traditional rhetoric of limiting or spacing childbirth [
Promisingly, in the past 40 years, family planning programs have played a major part in raising the prevalence of contraceptive from less than 10% to 60% and reducing fertility in developing countries from six to three births per woman [
Ethiopia with an estimated total population of over 90 million based on 2007 census projection [
However, a study on proximate fertility determinants in Gondar [
In general, for a very long period, fertility has remained high and FP coverage has stagnated at its low coverage in most parts of Africa either due to a poorly fitting policy design or intervention that understands the structural determinants of fertility in these nations. Some of these factors include economic inequality and livelihood insecurity, poverty, gender inequality, lack of education, cultural and religious barriers, ethnic differences, urbanization, and others that might be context specific. Indeed there are overwhelming evidences confirming sociodemographic characteristics as being very important factors to affect FP utilization among Ethiopians. However, past studies in the country that involve health behavior had tried to approach factors that affect health utilization only at an individual level. However, human population has complex social arrangement where group behaviors can also affect individual’s health behavior, which applies to FP service too. Hence, it is very important to see the effects of group behaviors and individual behaviors together to understand the complex nature of FP programs in the country for both individual and community based interventions and national level policy directions. This study has utilized a multistage data from EDHS 2011 to analyze group level context and individual factors that affect FP service using a mixed effects multilevel modeling.
Before conducting a multilevel study design, understanding the community living arrangement and data structure is very important. The Ethiopian community is administratively structured under 11 regions and two city administrations basically based on ethnic classification. However, practically the way regions, ethnic, religious, and other social groupings are categorized does not have a clear demarcation. For instance, it is easy to notice that ethnic boundaries go beyond regional differences resulting in ethnic division that is not nested under region and vice versa.
The DHS data is already designed in a two-stage cluster data (Figure
Two-stage cluster sampling and sampling data frame EDHS 2011.
The primary sampling units are selected randomly [
DHS questionnaires allow different units of analysis, households, household members, women, children, and couples being the major categories. This research is based on individual women’s data which contains record for every eligible woman for the study. The model in this study has tried to explore possible group effects that might affect FP utilization (Figure
Multilevel structure of predictors of family planning utilization.
According to EDHS 2011 methodology, a nationally representative sample of about 18,500 households was selected and all women aged 15–49 and all men aged 15–59 in these households were eligible for the individual interview module of the survey; however, the final respondents for the Ethiopian 2011 survey were 16515. The SAS version of this data was then downloaded from the DHS website after getting approval through online registration. Out of these respondents, a reproductive age woman should be sexually active and nonpregnant and should not be postpartum abstaining to be eligible for FP service. This resulted in final 8906 observations for further analysis using R programming software [
The main dependent variable in this study is current FP utilization, a binary variable. This response is then modeled to historical predictor variables that were selected based on existing evidences (see Table
Predictor variables for current family planning utilization.
Sn | Variable | Var. names | Variable labels |
---|---|---|---|
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Current FP utilization | FP.current | 0 = no, 1 = yes | |
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Age | Age | Numeric | |
Respondent education | Highest.educ | None, primary, secondary, and higher | |
Religion | Religion | Orthodox, Catholic, Protestant, Muslim, traditional, and other | |
Respondent occupation | Resp.ocupn | Agricultural, manual, not working, professional, sales, and services | |
Income | income | Poorest, poorer, middle, richer, and richest | |
Residence type | Residence.type | Urban, rural | |
Household size | hhld.number | <2, 3–5, 6–9, ≥10 | |
Husband occupation | Husband.ocupn | Agricultural, manual, not working, professional, sales, and services | |
Husband education | Husband.educ | None, primary, secondary, and higher | |
Hhld. relation | Hhld.relation | Head, wife, daughter, sister, other relatives, and others | |
Hhld. head sex | Hhld.head.sex | Male, female | |
Media exposure | Media exposure | Cont. 0–6 | |
FP knowledge | FP.knowledge | No, modern, traditional, and folkloric | |
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Highest income proportion | prop.inc | Proportion of respondents in the richest income group | |
Proportion. Media user | prop.media | Proportion of media utilizer above or equal to 3 (respondents) | |
Proportion extension visit | prop.xt | Proportion respondents having health extension visit per cluster | |
Primary complete proportion | prop.primary | Proportion of respondents who completed primary schools per cluster |
The random intercept model is used to model unobserved heterogeneity in the overall FP utilization by introducing random effects at a cluster level. In this model the intercept is the only random effect that the groups differ with respect to the average value of the response variable, but the relation between explanatory and FP utilization is assumed to be constant between clusters. Current FP utilization is a binary response variable coded as yes or no. Running a linear regression has multiple drawbacks for such a binary variable and transformation of the response variable needs a logistic link function. The resulting log odds value
It is assumed that the residual
This is an extended method of multilevel analysis where individual predictor variables are assumed to vary across groups too. In a practical situation it is difficult to find predictors that do not vary across groups at a community level. Hence, it is important to fit some variables that are assumed to be varying across different clusters. The model looks like the following for a binary response variable:
The major objective of this study is to analyze the structural determinants of FP service utilization in Ethiopia.
Family planning utilization is positively affected by education and income status. Women employment status improves better family planning service utilization. Husband education and employment promote better FP service utilization. Access to media services and community health services improve FP service utilization. Structural factors have contextual effect on family planning service.
This study uses a total of 8906 individual women’s data from the 2011 Ethiopian demographic and health survey, excluding those who are not sexually active within the last month of interview for nonmenopausal reasons. The result shows that around 2495 (28%) women were current utilizers of FP. Table
Cross tabulation of current family planning users to sociodemographic characters.
Unweighted | Weighted | OR | lCI | uCI |
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---|---|---|---|---|---|---|---|---|---|---|
Total | FP (yes) | FP (%) | Total | FP (yes) | FP (%) | |||||
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No | 5574 | 1056 | 18.95% | 6061.51 | 1402.39 | 23.14% | 1.00 | NA | NA | NA |
Elementary | 2536 | 947 | 37.34% | 2689.17 | 1002.34 | 37.27% | 1.97 | 1.79 | 2.18 | 0.0000 |
Secondary | 796 | 492 | 61.81% | 626.63 | 404.93 | 64.62% | 6.07 | 5.10 | 7.22 | 0.0000 |
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Tigray | 870 | 212 | 24.37% | 552.80 | 138.24 | 25.01% | 1.00 | NA | NA | NA |
Afar | 703 | 59 | 8.39% | 78.50 | 9.23 | 11.75% | 0.40 | 0.20 | 0.82 | 0.0094 |
Amhara | 1185 | 391 | 33.00% | 2475.65 | 859.91 | 34.73% | 1.60 | 1.29 | 1.97 | 0.0000 |
Oromiya | 1301 | 357 | 27.44% | 3675.66 | 1001.14 | 27.24% | 1.12 | 0.91 | 1.38 | 0.2803 |
Somali | 512 | 24 | 4.69% | 180.29 | 8.94 | 4.96% | 0.16 | 0.08 | 0.31 | 0.0000 |
Ben.-gumuz | 785 | 198 | 25.22% | 108.10 | 30.19 | 27.92% | 1.16 | 0.73 | 1.84 | 0.5468 |
SNNP | 1207 | 310 | 25.68% | 1893.47 | 518.83 | 27.40% | 1.13 | 0.91 | 1.41 | 0.2754 |
Gambela | 717 | 203 | 28.31% | 44.08 | 20.32 | 46.09% | 2.56 | 1.38 | 4.78 | 0.0068 |
Harari | 527 | 191 | 36.24% | 23.32 | 8.49 | 36.39% | 1.72 | 0.72 | 4.08 | 0.3273 |
Addis Ababa | 575 | 370 | 64.35% | 313.41 | 202.02 | 64.46% | 5.44 | 4.03 | 7.35 | 0.0000 |
Dire Dawa | 524 | 180 | 34.35% | 32.02 | 12.38 | 38.66% | 1.89 | 0.90 | 3.95 | 0.1430 |
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Urban | 2118 | 1112 | 52.50% | 1671.49 | 905.64 | 54.18% | 1.00 | NA | NA | NA |
Rural | 6788 | 1383 | 20.37% | 7705.81 | 1904.02 | 24.71% | 0.28 | 0.25 | 0.31 | 0.0000 |
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15–19 | 727 | 194 | 26.69% | 721.42 | 189.67 | 26.29% | 1.00 | NA | NA | NA |
20–24 | 1660 | 556 | 33.49% | 1715.24 | 617.17 | 35.98% | 1.58 | 1.30 | 1.91 | 0.0000 |
25–29 | 2299 | 675 | 29.36% | 2392.94 | 733.87 | 30.67% | 1.24 | 1.03 | 1.50 | 0.0257 |
30–34 | 1509 | 442 | 29.29% | 1584.44 | 532.94 | 33.64% | 1.42 | 1.17 | 1.73 | 0.0005 |
35–39 | 1350 | 387 | 28.67% | 1417.78 | 425.69 | 30.03% | 1.20 | 0.98 | 1.47 | 0.0772 |
40–44 | 808 | 169 | 20.92% | 868.51 | 204.59 | 23.56% | 0.86 | 0.69 | 1.09 | 0.2211 |
45–49 | 553 | 72 | 13.02% | 676.97 | 105.73 | 15.62% | 0.52 | 0.40 | 0.68 | 0.0000 |
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Orthodox | 3360 | 1342 | 39.94% | 4059.60 | 1490.70 | 36.72% | 1.00 | NA | NA | NA |
Muslim | 3551 | 640 | 18.02% | 2863.70 | 588.94 | 20.57% | 0.45 | 0.40 | 0.50 | 0.0000 |
Christians | 1845 | 502 | 27.21% | 2284.04 | 710.99 | 31.13% | 0.78 | 0.70 | 0.87 | 0.0000 |
Others | 150 | 11 | 7.33% | 169.97 | 19.03 | 11.19% | 0.22 | 0.13 | 0.35 | 0.0000 |
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Amhara | 2084 | 934 | 44.82% | 2894.14 | 1134.43 | 39.20% | 1.00 | NA | NA | NA |
Oromo | 2128 | 646 | 30.36% | 3229.96 | 879.24 | 27.22% | 0.58 | 0.52 | 0.65 | 0.0000 |
SNNP | 770 | 239 | 31.04% | 959.41 | 231.64 | 24.14% | 0.49 | 0.42 | 0.58 | 0.0000 |
Other | 3874 | 663 | 17.11% | 2254.18 | 556.28 | 24.68% | 0.51 | 0.45 | 0.57 | 0.0000 |
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Poorest | 2272 | 219 | 9.64% | 1857.11 | 276.71 | 14.90% | 1.00 | NA | NA | NA |
Poorer | 1476 | 282 | 19.11% | 1916.25 | 433.22 | 22.61% | 1.67 | 1.41 | 1.97 | 0.0000 |
Middle | 1407 | 331 | 23.53% | 1920.16 | 494.54 | 25.76% | 1.98 | 1.68 | 2.33 | 0.0000 |
Richer | 1436 | 442 | 30.78% | 1782.34 | 591.85 | 33.21% | 2.84 | 2.42 | 3.34 | 0.0000 |
Richest | 2315 | 1221 | 52.74% | 1901.45 | 1013.34 | 53.29% | 6.52 | 5.57 | 7.62 | 0.0000 |
Age is one of the determining factors in most health services. A bivariate cross tabulation (Table
A further disaggregation of the data to ethnic level shows that people from Amhara origin have higher odds of FP use (OR = 2.00) than other ethnic groups. However, a detailed view of the specific ethnic variations shows that far lower from the average Neuer, Afar, Somali, Mejenger, and Derashe communities are found and higher from the average there are Keficho, Amhara, Guraghe, and Dauro communities (Figure
Random intercepts by ethnic grouping (the codes on the column represent ethnic codes for Ethiopia according to DHS 2011).
Being urban and rural resident has also created a difference of about 54.4 percent in FP service utilization from 24.71 percent in rural resident to 54.18 percent in urban resident woman. From a religion perspective Orthodox Christians have about 36.72 percent FP practice, much better than other religions, 31.13% in other Christians, 20.57% in Muslims, and 11.19% in other religious followers.
There are ample studies that show the number of offspring as the other determining factor in fertility. In this study, women with one under one child are the highest consumers of FP [37.8%] while it shows a decline for mothers having two or more children. This proportion almost matches the effect of number of household members on FP consumption. FP use decreases as the number of household members’ increases, which indeed might show the growing practice of FP utilization in newly married couples unlike the trend that has existed for long among their mothers, owing to improvements in educational status and other factors that change their behavior, but it needs to be statistically confirmed.
On the other hand, relation to household head that might point the level of access and authority the woman has to resources shows that being a sister or a daughter to the household head has the lowest rate of FP consumption. However, it is very important to understand how a married woman can be with her family members in the local setup. Traditionally in most Ethiopian cultures, a woman goes to her husband’s family, where the husband usually resides. However, if separated or divorced or if the husband goes to her family (rarely), she will live with her father, or her brother or sisters and other relatives. The same reasoning can be applied to a woman living alone or being a head of a household in having a lower FP use. Table
Current family planning utilization with family size and husband characteristics.
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Secondary | 1303 | 636 | 48.81% | 985.63 | 531.55 | 53.93% | 3.98 | 3.45 | 4.60 | 0.0000 |
Elementary | 3165 | 1019 | 32.20% | 3728.81 | 1193.42 | 32.01% | 1.60 | 1.45 | 1.77 | 0.0000 |
No | 4142 | 714 | 17.24% | 4451.65 | 1010.93 | 22.71% | 1.00 | NA | NA | NA |
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Agricultural | 1881 | 450 | 23.92% | 2679.10 | 686.08 | 25.61% | 1 | NA | NA | NA |
Manual | 741 | 246 | 33.20% | 760.79 | 290.16 | 38.14% | 1.79 | 1.51 | 2.12 | 0.0000 |
Not working | 4370 | 1007 | 23.04% | 3981.35 | 1050.82 | 26.39% | 1.04 | 0.93 | 1.16 | 0.4770 |
Professional | 299 | 191 | 63.88% | 245.27 | 157.06 | 64.04% | 5.17 | 3.93 | 6.81 | 0.0000 |
Sales and services | 1519 | 571 | 37.59% | 1631.09 | 606.31 | 37.17% | 1.72 | 1.51 | 1.96 | 0.0000 |
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Agricultural | 5894 | 1173 | 19.90% | 7022.70 | 1654.42 | 23.56% | 1 | NA | NA | NA |
Manual | 788 | 402 | 51.02% | 709.44 | 356.93 | 50.31% | 3.29 | 2.81 | 3.84 | 0.0000 |
Not working | 141 | 19 | 13.48% | 67.58 | 13.94 | 20.63% | 0.84 | 0.47 | 1.52 | 0.6670 |
Professional | 627 | 287 | 45.77% | 469.02 | 268.22 | 57.19% | 4.33 | 3.58 | 5.25 | 0.0000 |
Sales and services | 1192 | 490 | 41.11% | 914.95 | 449.79 | 49.16% | 3.14 | 2.73 | 3.61 | 0.0000 |
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0 | 2268 | 739 | 32.58% | 2283.74 | 729.99 | 31.96% | 1.00 | NA | NA | NA |
1 | 3233 | 1167 | 36.10% | 3473.73 | 1312.14 | 37.77% | 1.29 | 1.16 | 1.44 | 0.0000 |
2 | 2617 | 508 | 19.41% | 2874.47 | 647.33 | 22.52% | 0.62 | 0.55 | 0.70 | 0.0000 |
3 | 669 | 70 | 10.46% | 676.88 | 109.57 | 16.19% | 0.41 | 0.33 | 0.51 | 0.0000 |
4 | 119 | 11 | 9.24% | 68.48 | 10.63 | 15.53% | 0.39 | 0.2 | 0.76 | 0.0037 |
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<2 | 657 | 222 | 33.79% | 589.91 | 191.71 | 32.50% | 1.00 | NA | NA | NA |
3–5 | 3912 | 1246 | 31.85% | 4248.12 | 1391.85 | 32.76% | 1.01 | 0.84 | 1.22 | 0.9255 |
6–9 | 3752 | 896 | 23.88% | 4029.96 | 1100.48 | 27.31% | 0.78 | 0.65 | 0.94 | 0.0092 |
≥10 | 585 | 131 | 22.39% | 509.31 | 125.61 | 24.66% | 0.68 | 0.52 | 0.89 | 0.0051 |
However, all these differences might also be due to other socioeconomic differences. Those with low number of children are most newly married couples, having a better education and access to FP, while those having many children are already mature, having low level of education, with low FP need and access. This can be supported by the increase in literacy level at an early age as more youngsters are attending school unlike their mothers and other elder family members; these all need a further analysis to understand this complex relationship.
A multilevel analysis of FP utilization using lme4 package [
Accordingly, as can be seen from the two model outputs in Table
Null model comparison for simple GLS and multilevel modeling.
Fixed model | Mixed model | |||||||
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Estimate |
|
Pr (> |
Estimate |
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Pr (> |
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(Intercept) | −0.94 (0.023) | −39.99 | <2 |
(Intercept) | −1.23 (0.069) | −17.83 | <2 |
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Random effects | Variance | Std.Dev. | ||||||
Null dev: | 10564 | 8905 df | Clust. | 2.178 | 1.476 | |||
Res.dev. | 10564 | 8905 df | AIC | BIC | logLik | Deviance | df.resid | |
AIC: | 10566 | 9202.4 | 9216.6 | −4599.2 | 9198.4 | 8904 |
The significance level (
Exploration of the random intercepts for FP utilization using caterpillar plot shows the presence of huge variation among each cluster (Figure
Caterpillar plot of cluster level random effects in log odds (the levels on the column are too many to visualize (650 clusters)).
The same graph points out that clusters which have odds of more than three in FP use are found in Addis Ababa, Amhara, Gambela, and Ben Gumuz, whereas the majority of Afar and Somali are found below 0.33, which is more than three standard deviations below the mean. Further review has revealed that certain clusters in Anyiwak community, Guraghe, Keficho, and some parts of Oromo community have better FP, which might be due to the small sample size or a real picture of better FP coverage that needs further quantitative and qualitative studies.
Exploration of variations within clusters using the variance partitioning for multilevel model shows that more than 39 percent of the variation in these observation is explained by the cluster level model. Note how the interclass correlation is calculated, keeping the individual level error constant, 3.29 [
In the upcoming sections, individual predictors were incorporated to the model to improve the prediction value and develop a best fit model. The first variable input into the model is age of a mother (Table
Structural determinants of family planning.
Summary (glmer 31) | glmer a | glmer b | glmer 1 | glmer 2 | glmer 3 | glmer 31 | ||||||
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OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser1) | OR | Coef. (ser) | |
(Intercept) | 0.29 | −1.23 (0.07) |
0.33 | −1.1 (0.07) |
0.58 | −0.55 (0.08) |
0.97 | −0.03 (0.09) | 2.62 | 0.96 (0.1) |
2.11 | 0.75 (0.1) |
|
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
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Orthodox | Ref. | |||||||||||
Muslim | 0.34 | −1.09 (0.09) |
0.37 | −1 (0.09) |
0.40 | −0.92 (0.09) |
0.99 | −0.01 (0.15) | ||||
relg.Others | 0.18 | −1.71 (0.35) |
0.20 | −1.61 (0.35) |
0.25 | −1.39 (0.35) |
0.40 | −0.92 (1.21) | ||||
Christians | 0.62 | −0.47 (0.11) |
0.75 | −0.29 (0.11) |
0.88 | −0.13 (0.1) | 1.35 | 0.3 (0.29) | ||||
Amhara | Ref. | |||||||||||
Oromo | 0.80 | −0.22 (0.11) |
0.81 | −0.21 (0.1) |
1.27 | 0.24 (0.14) | ||||||
SNNP | 0.70 | −0.35 (0.15) |
0.64 | −0.44 (0.14) |
0.85 | −0.17 (0.2) | ||||||
ethn.Other | 0.31 | −1.17 (0.11) |
0.35 | −1.04 (0.1) |
0.52 | −0.65 (0.12) |
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ethn.miss | 0.49 | −0.7 (0.39) | 0.53 | −0.64 (0.39) | 0.58 | −0.55 (0.83) | ||||||
Urban | Ref. | |||||||||||
Rural | 0.21 | −1.55 (0.11) |
0.23 | −1.47 (0.1) |
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Amhara: Orthodox | Ref. | |||||||||||
Muslim: Oromo | 0.27 | −1.3 (0.21) |
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relg.other: Oromo | 0.48 | −0.73 (1.39) | ||||||||||
Christian: Oromo | 0.47 | −0.76 (0.34) |
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Muslim: SNNP | 0.50 | −0.69 (0.3) |
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relg.other: SNNP | 0.42 | −0.87 (1.39) | ||||||||||
Christian: SNNP | 0.45 | −0.81 (0.38) |
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Muslim: ethn.other | 0.21 | −1.58 (0.22) |
||||||||||
relg.other: ethn.other | 0.73 | −0.32 (1.34) | ||||||||||
Christian: ethn.other | 0.64 | −0.45 (0.32) | ||||||||||
Muslim: ethn.miss | 0.61 | −0.49 (0.99) | ||||||||||
relg.other: ethn.miss | 0.00 | −9.7 (366.71) | ||||||||||
Christian: ethn.miss | 0.54 | −0.61 (1.1) | ||||||||||
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glmer a | glmer b | glmer 1 | glmer 2 | glmer 3 | glmer 31 | |||||||
|
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AIC | 9202 | 9152 | 9011 | 8885 | 8692 | 8643 | ||||||
BIC | 9217 | 9174 | 9053 | 8956 | 8770 | 8807 | ||||||
logLik | −4599 | −4573 | −4499 | −4432 | −4335 | −4299 | ||||||
Deviance | 9198 | 9146 | 8999 | 8865 | 8670 | 8597 | ||||||
var | 2.178 | 2.212 | 1.743 | 1.315 | 0.835 | 0.706 | ||||||
ICC | 0.398 | 0.402 | 0.346 | 0.286 | 0.202 | 0.177 |
The significance level (
The addition of religion and ethnicity as predictors further improves our model prediction and reduces the deviance from the null model. In this model Orthodox from religion and Amhara from ethnic category are set to be reference populations. Accordingly, compared to Orthodox religion which has relatively an improved utilization rate in FP, all that other religions have relatively lower utilization rate in FP service. Being Muslim is found to be significantly lower in FP utilization (OR = 0.34,
However, by observing Figure
Regional variations of different religions in Ethiopia.
Hence, religion as one of the structural factors is not found to be statistically significant factor at 5 percent level of significant for FP use; this is in line with the finding from [
On the other hand, analysis of ethnic differences shows that compared to Amhara, most of the other ethnic categories used in this model have a lower FP practice at a cluster level, even if it is only the other ethnic groups that include Afar, Somali, and Benishangul were found to be significantly lower in FP utilization (OR = 0.31,
In model four, it was found that urbanities have a much more probability of using FP with predicted values of 35.7% for the rural and 72.3% percent for the urban community, keeping ethnicity and religion constant. Assuming all other predictors constant, the odds of using FP service in rural communities is significantly lower than the urban residents (OR = 0.21,
An intermediate model that includes the combination of age, ethnicity, religion, residence, and an interaction term between ethnicity and religion explains a major part of both the within cluster and between cluster differences. In particular, ethnicity, religion, and rural urban differences explain the major between cluster variations, evidenced by a fall in ICC from 39.8% to 20.2%. The model deviance also improves from 9198 to 8670. This implies that the group effect of these predictors is very strong in justifying the difference in FP use. This might probably be further due to differences in access to infrastructure, human resource, and various cultural differences which is beyond the scope of this study to further disentangle. However, analysis of the structural determinants without including the group level prediction would lead to wrong conclusions as far as big share of the variations is explained by these variables. As opposed to most literatures which say that religion is a determining factor for FP, our finding indicates that the effect of religion on FP is dependent on ethnic variations. A fitted regression line with random intercept model for the intermediate model is shown below:
The upcoming section will address the effects of education and income differences on FP services. Among many of the socioeconomic predictors of FP utilization, income is believed to be a key in leading to a better education, access to health facility, access to information, and better living standards. According to EDHS, the overall Gini Coefficient for Ethiopia is 0.23 with least equitable distributions being high in urban setups than rural, in Afar and Gambela than other regions. Keeping ethnicity, religion, and age constant, as can be seen in the following model, education and income were significant determinants for FP. For education, women with secondary and above level were taken as reference group and the FP use of people having no education and primary was compared against the reference. The finding shows that women with no education or primary education were found to have lower odds of FP use (OR = 0.45 (0.11) and OR = 0.72 (0.1), resp.). Similar other studies have showed that the effects of education on FP were higher for women with secondary and above education than with no education [
On the other hand, taking the richest income group as a reference group, almost all other income categories have a lower proportion of FP service utilization (OR = 0.24 (0.14) for the poorest and OR = 0.39 (0.14) for the poorer). In other words the odds of FP utilization are four times higher in the richest group than the poorest group at a cluster level. The interaction of these predictors did not significantly affect the output of the model, which is supported by the small deviance and almost similar between variances (0.614) which is not significant. However, addition of income variable to the model reduces the variance from 0.76 to 0.63, showing how income has a strong variation within clusters and takes the biggest share in explaining the cluster level differences. The intercluster correlation shows that addition of income predictor reduces the between cluster variation from 18.9 percent to 16.2 percent. The cluster effect still explains more than 16 percent of the variation in FP utilization between the clusters. On this ground, this research appears to validate the view that education and income are strong structural factor that affects the group behavior of communities much more than individual decisions for FP (Table
Income and education on FP service utilization controlling for other predictors.
>summary (glmer 52) $coef. | glmer 3 | glmer 4 | glmer 5 | glmer 51 | ||||
---|---|---|---|---|---|---|---|---|
OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | |
(Intercept) | 2.62 | 0.96 (0.1) |
3.43 | 1.23 (0.12) |
2.74 | 1.01 (0.1) |
3.36 | 1.21 (0.11) |
|
0.97 | −0.03 (0) |
0.98 | −0.02 (0) |
0.98 | −0.03 (0) |
0.98 | −0.02 (0) |
Orthodox | ||||||||
Muslim | 0.40 | −0.92 (0.09) |
0.43 | −0.83 (0.09) |
0.41 | −0.9 (0.09) |
0.43 | −0.83 (0.09) |
relg.other | 0.25 | −1.39 (0.35) |
0.26 | −1.35 (0.35) |
0.31 | −1.17 (0.35) |
0.31 | −1.16 (0.35) |
Christian | 0.88 | −0.13 (0.1) | 0.86 | −0.16 (0.1) | 0.94 | −0.06 (0.1) | 0.92 | −0.09 (0.1) |
Amhara | ||||||||
Oromo | 0.81 | −0.21 (0.1) |
0.83 | −0.19 (0.1) | 0.77 | −0.26 (0.1) |
0.78 | −0.24 (0.1) |
SNNP | 0.64 | −0.44 (0.14) |
0.67 | −0.4 (0.14) |
0.61 | −0.49 (0.14) |
0.63 | −0.46 (0.14) |
Ethn.other | 0.35 | −1.04 (0.1) |
0.36 | −1.03 (0.1) |
0.38 | −0.96 (0.1) |
0.39 | −0.95 (0.1) |
Ethn.miss | 0.53 | −0.64 (0.39) | 0.58 | −0.54 (0.39) | 0.54 | −0.61 (0.39) | 0.59 | −0.53 (0.39) |
Urban | ||||||||
Rural | 0.21 | −1.55 (0.11) |
0.29 | −1.23 (0.11) |
0.53 | −0.64 (0.14) |
0.60 | −0.51 (0.14) |
Secondary | ||||||||
Educnno | 0.45 | −0.8 (0.11) |
0.53 | −0.63 (0.11) |
||||
Elementary | 0.72 | −0.33 (0.1) |
0.77 | −0.26 (0.1) |
||||
Richest | ||||||||
Poorest | 0.21 | −1.58 (0.14) |
0.24 | −1.41 (0.14) |
||||
Poorer | 0.34 | −1.09 (0.14) |
0.39 | −0.95 (0.14) |
||||
Inc.middle | 0.41 | −0.89 (0.13) |
0.47 | −0.76 (0.13) |
||||
Incomericher | 0.59 | −0.53 (0.12) |
0.65 | −0.43 (0.12) |
||||
|
||||||||
glmer 3 | glmer 4 | glmer 5 | glmer 51 | |||||
|
||||||||
AIC | 8692 | 8626 | 8556 | 8517 | ||||
BIC | 8770 | 8718 | 8663 | 8638 | ||||
logLik | −4335 | −4300 | −4263 | −4242 | ||||
Deviance | 8670 | 8600 | 8526 | 8483 | ||||
Var | 0.835 | 0.764 | 0.635 | 0.610 | ||||
ICC | .202 | 0.189 | 0.162 | 0.156 |
The significance level (
Another model was developed to evaluate FP knowledge, media utilization, and visit to the household by a health extension worker on FP practice. Media utilization was measured using categorical variables of radio listening, reading newspaper, and watching television. These categorical media level predictors were combined to give a new composite numerical media predicator, named “media” that has six categories. The maximum value of 6 is given for those who have a value of two each for radio listening, television watching, and news reading, exemplified by people who listen radio at least once a week (2 points) and who read newspaper less than once a week (1 point) and who do not watch television (0 point) will have a total of
Similarly, the input of media at the individual cluster is found to be a significant predictor of FP use with a reduction in group variance from 0.83 to 0.74, which tells that media consumption has significant between cluster variance. The small effect size might tell us the need to well design and develop a very strong media program or the need to expand the reach and frequency of exposure to result in a much pronounced effect. Or it might also tell us the media utilization is relatively weak and innovative ways of reaching the community should be designed to increase access to media and bring behavioral change at a community level. In a similar fashion, addition of health extension visit into the model model (Table
Structural determinants of family planning in Ethiopia, effect of access to media, and home visit by HEW.
(glmer 73) $coef. | glmer 3 | glmer 6 | glmer 71 | glmer 73 | ||||
---|---|---|---|---|---|---|---|---|
OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | |
(Intercept) | 2.62 | 0.96 (0.1) |
1.43 | 0.36 (0.12) |
2.43 | 0.89 (0.1) |
0.00 | −15.83 (508.68) |
|
0.97 | −0.03 (0) |
0.98 | −0.02 (0) |
0.97 | −0.03 (0) |
0.98 | −0.02 (0) |
Orthodox | ||||||||
Muslim | 0.40 | −0.92 (0.09) |
0.43 | −0.85 (0.09) |
0.40 | −0.91 (0.09) |
0.94 | −0.8 (0.08) |
relg.other | 0.25 | −1.39 (0.35) |
0.28 | −1.28 (0.35) |
0.25 | −1.38 (0.35) |
1.08 | −1.13 (0.36) |
christians | 0.88 | −0.13 (0.1) | 0.92 | −0.09 (0.1) | 0.91 | −0.1 (0.1) | 0.94 | −0.06 (0.1) |
Amhara | Ref | |||||||
Oromo | 0.81 | −0.21 (0.1) |
0.80 | −0.22 (0.1) |
0.81 | −0.21 (0.1) |
0.79 | −0.23 (0.1) |
SNNP | 0.64 | −0.44 (0.14) |
0.64 | −0.45 (0.14) |
0.65 | −0.44 (0.14) |
0.64 | −0.45 (0.14) |
ethn.other | 0.35 | −1.04 (0.1) |
0.36 | −1.02 (0.1) |
0.35 | −1.05 (0.1) |
0.38 | −0.97 (0.1) |
Urban | Ref | |||||||
Rural | 0.21 | −1.55 (0.11) |
0.31 | −1.16 (0.11) |
0.21 | −1.56 (0.11) |
0.45 | −0.81 (0.09) |
Media | 1.21 | 0.19 (0.02) |
1.19 | .17 (0.022) |
||||
xt.visit | 1.50 | 0.41 (0.07) |
1.39 | 0.33 (0.07) |
||||
|
||||||||
glmer 6 | glmer 71 | glmer 73 | ||||||
|
||||||||
AIC | 8561 | 8661 | 8463 | |||||
BIC | 8639 | 8746 | 8562 | |||||
logLik | −4270 | −4319 | −4217 | |||||
Deviance | 8539 | 8637 | 8435 | |||||
var | 0.749 | 0.815 | 0.680 | |||||
ICC | 0.185 | 0.199 | 0.171 |
The significance level (
In the upcoming model the effects of husband education, occupation, relation to household head, household family size, and respondent’s occupation were reviewed. As can be seen from Table
Structural determinants of FP and partner’s related predictors.
>summary (glmer 84) | glmer 3 | glmer 80 | glmer 81 | glmer 82 | glmer 83 | |||||
---|---|---|---|---|---|---|---|---|---|---|
OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | |
(Intercept) | 2.62 | 0.96 (0.1) |
2.28 | 0.83 (0.13) |
1.67 | 0.51 (0.15) |
1.27 | 0.24 (0.16) | 0.90 | −0.11 (0.19) |
|
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.98 | −0.03 (0) |
0.98 | −0.02 (0) |
0.98 | −0.03 (0) |
Orthodox | ref | |||||||||
Muslim | 0.40 | −0.92 (0.09) |
0.42 | −0.86 (0.09) |
0.42 | −0.87 (0.09) |
0.44 | −0.82 (0.09) |
0.44 | −0.82 (0.09) |
relg.Others | 0.25 | −1.39 (0.35) |
0.26 | −1.35 (0.35) |
0.25 | −1.4 (0.37) |
0.24 | −1.44 (0.37) |
0.24 | −1.41 (0.37) |
Christians | 0.88 | −0.13 (0.1) | 0.91 | −0.09 (0.1) | 0.91 | −0.09 (0.11) | 0.87 | −0.14 (0.11) | 0.86 | −0.15 (0.11) |
Amhara | ref | |||||||||
Oromo | 0.81 | −0.21 (0.1) |
0.80 | −0.23 (0.1) |
0.77 | −0.26 (0.11) |
0.72 | −0.32 (0.11) |
0.72 | −0.34 (0.11) |
SNNP | 0.64 | −0.44 (0.14) |
0.63 | −0.47 (0.14) |
0.58 | −0.55 (0.14) |
0.55 | −0.6 (0.15) |
0.54 | −0.61 (0.14) |
ethn.other | 0.35 | −1.04 (0.1) |
0.35 | −1.06 (0.1) |
0.32 | −1.13 (0.1) |
0.32 | −1.14 (−0.1) |
0.32 | −1.13 (0.1) |
Ethn.miss | 0.53 | −0.64 (0.39) | 0.39 | −0.95 (0.46) |
0.40 | −0.91 (0.46) |
0.37 | −1 (0.47) |
0.37 | −1 (0.47) |
Urban | ref | |||||||||
Rural | 0.21 | −1.55 (0.11) |
0.24 | −1.44 (0.11) |
0.32 | −1.13 (0.13) |
0.35 | −1.04 (0.13) |
0.36 | −1.01 (0.13) |
resp. Agriculture | ref | |||||||||
resp. manual | 1.34 | 0.29 (0.12) |
1.26 | 0.23 (0.12) | 1.25 | 0.22 (0.13) | 1.27 | 0.24 (0.12) | ||
resp. notworking | 0.92 | −0.08 (0.08) | 0.88 | −0.12 (0.09) | 0.88 | −0.13 (0.09) | 0.87 | −0.14 (0.09) | ||
resp. proff | 1.82 | 0.6 (0.16) |
1.46 | 0.38 (0.18) |
1.39 | 0.33 (0.18) | 1.40 | 0.34 (0.18) | ||
resp. sales and services | 1.24 | 0.21 (0.1) |
1.22 | 0.2 (0.1) | 1.20 | 0.18 (0.1) | 1.22 | 0.2 (0.1) | ||
Husb.Agriculture | ref | |||||||||
Husb.mannual | 1.55 | 0.44 (0.12) |
1.45 | 0.37 (0.13) |
1.54 | 0.43 (0.13) |
||||
Husb.notworking | 0.85 | −0.16 (0.29) | 0.79 | −0.23 (0.29) | 0.91 | −0.09 (0.29) | ||||
Husb.proff | 1.73 | 0.55 (0.14) |
1.54 | 0.43 (0.15) |
1.58 | 0.46 (0.15) |
||||
husb.sales and services | 1.58 | 0.46 (0.1) |
1.52 | 0.42 (0.11) |
1.58 | 0.46 (0.11) |
||||
husb.educnno | ||||||||||
husb.elementary | 1.53 | 0.42 (0.07) |
1.52 | 0.42 (0.07) |
||||||
husb.secondary | 1.53 | 0.43 (0.11) |
1.63 | 0.49 (0.11) |
||||||
hhld.rltnhead | ref | |||||||||
hhld.rltnwife | 1.52 | 0.42 (0.1) |
||||||||
hhld.rltnrelative | 0.77 | −0.26 (0.33) | ||||||||
hhld.rltnOthers | 0.83 | −0.19 (0.15) | ||||||||
hhld.cat <2 | ref | |||||||||
hhld.cat 3–5 | 1.92 | 0.65 (0) |
||||||||
hhld.cat 6–9 | 2.34 | 0.85 (0) |
||||||||
hhld.cat ≥10 | 2.32 | 0.84 (0) |
||||||||
|
||||||||||
glmer 80 | glmer 81 | glmer 82 | glmer 83 | |||||||
|
||||||||||
AIC | 8571 | 8261 | 8137 | 8099 | ||||||
BIC | 8677 | 8395 | 8285 | 8268 | ||||||
logLik | −4270 | −4111 | −4048 | −4025 | ||||||
Deviance | 8541 | 8223 | 8095 | 8051 | ||||||
Var | 0.818 | 0.815 | 0.764 | 0.749 | ||||||
ICC | 0.199 | 0.199 | 0.188 | 0.185 |
The significance level (
Regarding the effect of husband education of FP, making those with no education as a reference, having elementary and secondary educated partners increases the odds of FP use by 1.53 (0.07) and 1.53 (0.11), respectively. The reduction in the between cluster variance following this predictor indicates the hidden contextual effect that partner’s education has on women FP use, beyond its significant effect within the cluster. A similar study in Ethiopia has revealed that partners educational status is an important factor for a woman’s FP utilization with
Relation of the respondent to the household head is also believed to affect the decision making power of the mother (Table
Household size is also well studied to be one of the determining factors for fertility. Our finding demonstrates that households that have a family size of greater than 6 have almost an odds value of 2.3 times FP utilization than those having two and less (OR = 2.3 (0.0)). The same applies for families having 3–5 to use FP much higher than those having 2 and less. Unlike the simple frequency table outcomes, the findings from the model indicate the theoretical assumption that families with higher family size use FP, much more than others; however, the group variance increases. This shows that FP utilization with respect to number of household sizes has a huge variation between clusters. These findings match a study in Pakistan where the number of living children and women educational status [
So far, the multilevel models we have considered have allowed the response probability to vary from group to group by including a group-level random term “
Table
Random slope outcomes.
Random slope model | glmer 3 | glmer 740 | glmer 7410 | glmer 7420 | glmer 7430 | glmer 751 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | OR | Coef. (ser) | |
(Intercept) | 2.62 | 0.96 (0.1) |
2.48 | 0.91 (0.1) |
2.74 | 1.01 (0.09) |
1.40 | 0.34 (0.11) |
3.44 | 1.24 (0.12) |
2.23 | 0.8 (0.14) |
|
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.97 | −0.03 (0) |
0.98 | −0.02 (0) |
0.98 | −0.02 (0) |
0.98 | −0.02 (0) |
Orthodox | ||||||||||||
Muslim | 0.4 | −0.92 (0.09) |
0.42 | −0.87 (0.09) |
0.40 | −0.91 (0.09) |
0.44 | −0.83 (0.09) |
0.43 | −0.83 (0.09) |
0.46 | −0.77 (0.09) |
relg.Others | 0.25 | −1.39 (0.35) |
0.26 | −1.35 (0.35) |
0.31 | −1.18 (0.36) |
0.29 | −1.25 (0.35) |
0.26 | −1.35 (0.35) |
0.33 | −1.11 (0.35) |
Christians | 0.88 | −0.13 (0.1) | 0.91 | −0.1 (0.1) | 0.92 | −0.08 (0.1) | 0.92 | −0.08 (0.1) | 0.86 | −0.15 (0.1) | 0.92 | −0.08 (0.1) |
Amhara | ref | |||||||||||
Oromo | 0.81 | −0.21 (0.1) |
0.78 | −0.25 (0.1) |
0.80 | −0.23 (0.1) |
0.85 | −0.17 (0.1) | 0.82 | −0.2 (0.1) | 0.79 | −0.24 (0.1) |
SNNP | 0.64 | −0.44 (0.14) |
0.62 | −0.48 (0.14) |
0.65 | −0.44 (0.14) |
0.71 | −0.34 (0.14) |
0.66 | −0.41 (0.14) |
0.69 | −0.36 (0.13) |
ethn.other | 0.35 | −1.04 (0.1) |
0.34 | −1.07 (0.1) |
0.39 | −0.94 (0.1) |
0.39 | −0.95 (0.1) |
0.35 | −1.04 (0.1) |
0.39 | −0.93 (0.1) |
Urban | ref | |||||||||||
Rural | 0.21 | −1.55 (0.11) |
0.22 | −1.5 (0.1) |
0.55 | −0.59 (0.13) |
0.30 | −1.2 (0.1) |
0.29 | −1.22 (0.11) |
0.62 | −0.47 (0.13) |
Richest | ||||||||||||
Poorest | 0.20 | −1.62 (0.14) |
0.28 | −1.28 (0.15) |
||||||||
Poorer | 0.31 | −1.17 (0.14) |
0.42 | −0.87 (0.14) |
||||||||
Middle | 0.38 | −0.97 (0.13) |
0.50 | −0.69 (0.14) |
||||||||
Richer | 0.56 | −0.57 (0.12) |
0.69 | −0.38 (0.12) |
||||||||
xt.visit | 1.52 | 0.42 (0.07) |
1.40 | 0.33 (0.07) |
||||||||
Media | 1.20 | 0.19 (0.02) |
1.08 | 0.08 (0.02) |
||||||||
Secondary | ||||||||||||
Educnno | 0.45 | −0.8 (0.11) |
0.62 | −0.47 (0.12) |
||||||||
Elementary | 0.72 | −0.33 (0.1) |
0.85 | −0.17 (0.11) | ||||||||
|
||||||||||||
glmer 740 | glmer 7410 | glmer 7420 | glmer 7430 | glmer 751 | ||||||||
|
||||||||||||
AIC | 8597 | 8496 | 8536 | 8580 | 8408 | |||||||
BIC | 8689 | 8609 | 8628 | 8679 | 8635 | |||||||
logLik | −4285 | −4232 | −4255 | −4276 | −4172 | |||||||
Deviance | 8571 | 8464 | 8510 | 8552 | 8344 | |||||||
Var | 1.463 | 1.124 | 1.295 | 0.937 | 1.352 | |||||||
prop.primary | 4.574 | 0.255 | 0.987 | 2.783 | 0.558 | |||||||
prop.xt | 0.684 | |||||||||||
prop.media | 1.460 | |||||||||||
prop.rich | 1.739 |
The significance level (
Variables that were included in the random slope model were generated using cluster level aggregation. Income, education, health extension workers visit, and media access were assumed to vary among clusters. As most of these predictors are categorical variables, a group level predictor is computed taking the proportion of a reference group within that cluster. For education, proportion of members that complete primary education is taken as reference, represented by “prop_primary.” Similarly for income category, the existing five income categories are reclassified as low, middle, and high. In a similar fashion with education, the proportion of high income is taken and its variability among the clusters is taken as a new variable. The proportion of health extension workers visited members and the proportion of people having media access are other two variables included in the model. A total of four variables are included to vary in the level two cluster analysis. Media variable was reclassified into weak and strong media follower based on the existing predictor variable and the proportion of strong media followers is the predictor variable that is included in the level two analyses. The result of the analysis is presented below:
Observation of the random slope outcome in Table
FP service in Ethiopia has displayed a fast improvement in coverage from 2000 to 2011. However, still fertility is about 5.8 in the rural setup and the level of regional variations in fertility and FP is very height resulting in a net effect that maintains the annual population growth of the population over 2.6, which will lead to a projected 188 million population by 2050, an increase of about 100 million in less than 40 years’ time. Though there is limited access to empirical raw data, the availability of DHS data has created access for academicians to investigate the existing factors that affect FP utilization. There is overwhelming evidence corroborating the individual effects of most socioeconomic factors that affect FP use. However, human being has a complex nature of social interaction that affects its individual and communal behaviors. This factor needs to be considered and structural factors that affect individual behavior and group behavior should be targeted differently for a better access to FP. In this paper, the authors put forward the claim that the effects of structural determinants of family planning have both direct and indirect effects at a community level to affect service utilization. In the authors view, inability to understand their indirect contextual effects leads to poor planning and interventions that do not address the primary causes.
This study analyzed 8906 individual women observation from 2011 DHS data using a multilevel modeling that helps understand group level difference. The results show that there is a big variation in FP service utilization both at individual observations and within group (around 39% of the variation in FP use is due to cluster level differences). The fixed effects model in this study propose that age, ethnicity, and residence were found to be important predictor variables for FP use within clusters. The effect of age (OR = 0.97,
In a similar fashion income is found to be a very strong determining factor for FP use (OR = 0.24, ser = 0.129 for the poorest class) and a huge group level effect. Keeping the other factors constant improving income level from the poorest community to a one level poorer scale would improve the odds of FP use from 0.218 to 0.422, an increase of more than 93%. However, education which is believed to improve FP use does not seem to be a strong factor to affect FP use differences within cluster while remaining to be a strong factor for the between cluster differences. These predictors that have strong communal influence need a strong national intervention and individual efforts will be less effective to bring change at a community level.
There are overwhelming evidences that support the role of partners support and his sociodemographic characters that determine FP use. This current research appears to support these ideas, and husband education and occupation were found to be very significant predictors (OR = 2.28 (0.16) for husband education and OR = 1.635 (0.15) for husband occupation). More importantly, husband occupation was found to have a strong between cluster variations that need a policy level intervention to address the gap. Despite these, respondent occupation is found to be insignificant in determining FP utilization, probably because of the low number of employed female workers nationally.
Controlling for the effects of sociodemographic characters, health extension worker visit and media were found to be very significant factors within cluster variations. However, their group variation is very small. The forgoing discussions imply that the FP utilization in Ethiopia is affected by a complex group and individual factors. While ethnicity, rural urban residence, access to media, income level, partners education and occupation, and visit by health extension workers are strong individual level factors that affect FP, still a significant portion of the variation at a group level is explained by income, FP knowledge gap, ethnicity, religion, and husband occupation. Further analysis of these differences at group level supports that the between cluster variation is strongly affected by income, education, and access to media. This research work is conducted to better understand the structural determinants of FP utilization in Ethiopia to design a national based media intervention project that promotes behavioral change for a better FP service utilization at community level. On these grounds the authors would like to recommend the following. Family planning interventions that address contextual differences across communities should be given attention with major emphasis to creating access to ethnic minorities. Improving livelihood and coverage of secondary and above education should be given much attention to bring about a sustainable FP utilization. Media interventions that try to improve FP utilization should be supported with strong community interventions. Improvement in urbanization and diversification of employment options should be a focus for policy planners and implementers to make use of FP programs. Education will improve family planning service utilization when the level of intervention is strong enough to bring community level differences. Empowerment of women needs to be well designed and measures to ensure its implementation should be strengthened to create access to FP service.
This paper is secondary data analysis for consumption of a Population Media Project in Ethiopia.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The project is implemented by planned population federation of Korea/PPFK/and the Population Development Directorate/PDD/under the National Planning Commission/NPC/of Ethiopia with the technical support from Seoul National University, while financially it is supported by KOICA. The researchers acknowledgement goes to PPFK for supporting financial grant and all other professionals and organizations involved for their technical support in this research.