DETERMINANTS OF FERTILITY STATUS AMONG REPRODUCTIVE AGE WOMEN IN RURAL ETHIOPIA: EVIDENCE FROM 2016 ETHIOPIAN DEMOGRAPHIC HEALTH SURVEY

Background: Fertility rate is a significant determinant of population growth worldwide. Global population is growing about 80 million people each year. Ethiopia is one of the most heavily populated countries in the world. Objective: The objective of this study was to assess the major determinants of fertility status among reproductive age women in rural Ethiopia. Methods: The study used 2016 Ethiopian Demographic and Health Surveys data. A total of 8464 rural women aged 15 to 49 years included in the analysis. Poison Regression model was used to determine factors moving fertility status of rural women. Results: The overall average children ever born to women in rural regions were 5.1 children per women. Poisson regression analysis revealed that region, age group of respondents, education level, religion, household wealth index, sex of the head of the household, contraceptive use, age at first birth and mass media were found to be statistically significant factors for fertility rate of women. Conclusion: To reduce the gap of fertility rate between regional women, it is important to renovate different factors. These factors could be access to education, informing about contraceptive use, informing about family planning, enhancing to mass media. Therefore, the government and concerned bodies should plan effective strategies to increase the experience of using contraceptive across the country with transport limited regions. In addition to this, it is important to develop and keep the access of family planning services and the government could also uphold an access of education system for those restricted regions.


Objective of the Study
The main objective of this study was to assess the determinants of fertility status among reproductive age women in rural Ethiopia evidence from EDHS2016.

Literature Review
Studies done in different countries identified various differentials of fertility. Different researchers have been identified numerous factors that are determinants of fertility among reproductive age women. Like contraceptive use, levels of education, religion, and household wealth index, sex of the head of the household, age at first birth, Gender preference, employment status, family size and mass media are the key factors in explaining the discrepancy of fertility.
A study employed in Sudan using Poisson Regression model showed a significant relationship between fertility rate and high level of education, wealth index and contraceptive use. The richest quintile was statistically significant at 5% level of significance, indicating that fertility rate was higher among poor people compared to the rich. Educated women had fewer children than uneducated women. The findings also suggested that women who did not use contraceptive method had more children than women who experienced contraceptive method [11]. A study conducted in Uganda also indicated that a woman's contraceptive behavior; marriage status, wealth status and region of residence are important predictors of fertility outcomes. The results also showed that higher education levels and urban residence are consistently associated with lower fertility rates and are positively associated with contraceptive use [19].
According to study done in Butajira, indicated that age at first birth, education, food shortage, family size and sex preference are an important factors of fertility rate. Educational status of women had also been consistently and significantly found to be negatively associated with fertility. Women who had never been into any formal education had 1.24 times more children compared to those who completed secondary and above level of education. Similarly women who had no sex preference to their children had about 9 percent higher fertility compared to those with sex preference [13]. The results of study done in Rwanda showed lower fertility among women with more education and with greater household wealth. The IRR was 0.96 0.90 0.66 respectively for women with primary, secondary and higher education levels compared to women with no education. Similarly, low fertility was associated with Household wealth (highest versus lowest quintile: IRR = 0.58, and having contraception use; IRR = 0.69. [24]. The multiple Poisson regression of CEB conducted in Nigeria showed women with secondary and higher education were about 5% and 23% times (IRR= 0.77, 95% CI: 0.75-0.80) less likely to have children as women with no formal education. The employed women were 1.03 times more likely to have children than unemployed women.
Respondents in richest wealth quintile were 0.92 times less likely to have as many children as the poorest respondents [19].

Data Source
Data obtained from the Ethiopian Demographic and Health Surveys (EDHS) conducted in 2016 were used. Surveys were conducted by Central Statistical Agency (CSA) of Ethiopia. The surveys were nationally representative cross sectional study that collected comparable demographic and health data on women aged 15-49 during the survey periods. The women were asked about their birth histories and this provided information about the total number of children ever born [9]. This study was based on secondary analysis of data among women aged 15-49 years collected in the 2016 demographic and health surveys. A total of 8464 rural women included in the analysis.

The Model
The outcome variable, fertility status is defined as the total number of children ever born (CEB) to women in the childbearing age (15-49 years). It is a count variable that takes a nonnegative integer value. Poisson regression Incidence Rate Ratio with 95 percent confidence interval (CI) is appropriate to assess the association of various predictors with dependent variable fertility [25]. Poisson regression deals with situations in which the dependent variable is a count and the expected value is similar to the variance for each observation. Since, total children ever born to women in the reproductive age group which is a count data is measured as the outcome variable for this study, the researcher adopted a generalized linear model with a natural log-link function (Poisson regression). The expected value of the count variable (y) conditional on a set of predictor variables x is given by.
This condition assures that E ( ⁄ ) > 0 Then the probability of children born is given as: , Where y = 0, 1, 2,…, n ………..……… (2) The maximum likelihood Poisson fertility equation is then specified as: The full model is therefore given as: The conditional mean and variance of the dependent variable are constrained to be equal for each observation.
Poisson regression is a nonlinear regression analysis of the Poisson distribution, where the analysis is highly suitable for analyzing discrete data (count) if the mean equal to the variance process. Poisson distribution is a limiting case of the binomial distribution when the number of trials becomes large while the expectation remains stable, i.e., the probability of success is very small.

Results and Discussion
Data analysis was done using SPSS version 20 for windows. The data was cleaned and preliminary analysis was done by the investigator. The overall significance of each covariate was first checked and those variables statistically significant in the bi-variate analysis were also included in multivariate Poisson regression model to compute Incidence Rate Ratio (IRR). Independent variables were; Region (regional state of respondents), Age (current age group of respondents), Education (highest education level attained by the respondents), family size of the household, religion (religion of respondents), household wealth index, sex of household head (sex of the head of the household), contraceptive use (current use of any contraceptive method), age at first birth (age at first start of delivery), employment status, gender preference and mass media.

Results of Descriptive Statistics
As we have seen from Table 1, the overall average children ever born to women in the reproductive age group was found to be 5.1 children per women (standard deviation = 2.6). Total 8464

Interpretation for Multivariable Analysis results
The results of the multivariable analysis are presented in Table 1&2. Analysis of Poisson regression showed the major significant factors of fertility status among rural women were identified. We can interpret the Incidence Relative Ratio of each factor depending on 95 percent confidence interval and P-value. If the confidence interval not includes 1 and the P-value is less than 0.05, then the variable is significant and can be interpreted as the average From the result, it was found that Educational status of women had also been consistently and significantly found to be negatively associated with fertility. Women who have no education have 24.4% higher average children ever born as compared to women who have attended secondary and higher education (IRR = 1.244, 95% CI, 1.178-1.315). Similarly, women who have attended primary education have 10.7% higher average children born than counterpart (IRR = 1.107, 95% CI, 1.048-1.170). likewise, women whose husband have no education have 5% higher average children born compared to women whose husband have secondary and higher education (IRR = 1.050, 95% CI, 1.018-1.083). The same, women whose husband have attended primary education have 4.7% higher average children born than counterpart (IRR = 1.047, 95% CI, 1.014-1.080). The other predictor variable of fertility rate is economic status of the household. Women in the poorly economic group had 2.7% higher average number of children than the expected number of children of women in the richly economic group (IRR = 1.027, 95% CI, 1.006-1.049). This indicates that women who have better facility leads to have the lower fertility rate. Contraceptive is also the most important factor of fertility of women. As the findings showed, women who did not use contraceptive had 9.2% greater average fertility compared to women who used contraceptive (IRR = 1.092, 95% CI, 1.070-1.113). The average number of children born for women who started their first birth before the age of 19 years old was 1.402 times than women who delivered their first birth in greater or equal to19 years old(RR = 1.402, 95% CI, 1.380-1.425). On the other hand, the average fertility of mothers whose age group is less than 25 was 68.5% lower than that of mothers whose age group is between 35 and 49. In the same way, mothers whose age group is between 25 and 34 had 36.9% lower average children born compared to the reference category. Mass media about family planning is also an important factor for fertility status. The average number of children born for women who got information from mass media was 0.961 times less likely than women who did not get mass media (IRR = 0.961, 95% CI, 0.932-0.990).