International Journal of Sexual and Reproductive Health Care
Research Article       Open ijsrhccess      Peer-Reviewed

Assessment of determinant factors of pregnancy termination among women of reproductive age group in Ethiopia: Evidence from 2016 Ethiopian Demographic and Health Survey

Berhanu Teshome Woldeamanuel*

Lecturer, Department of Statistics, Salale University, P.O.Box 254, Fitche, Oromiya, Ethiopia
*Corresponding author: Berhanu Teshome Woldeamanuel, Lecturer, Department of Statistics, Salale University, P.O.Box 254, Fitche, Oromiya, Ethiopia, E-mail: imran2naila@yahoo.com
Received: 15 July, 2019 | Accepted: 03 August, 2019 | Published: 03 August, 2019
Keywords: Abortion; Logistic regression; Maternal mortality; Pregnancy termination; Prevalence

Cite this as

Woldeamanuel BT (2019) Assessment of determinant factors of pregnancy termination among women of reproductive age group in Ethiopia: Evidence from 2016 Ethiopian Demographic and Health Survey. Int J Sex Reprod Health Care 2(1): 010-015. DOI: 10.17352/ijsrhc.000005

Background: Pregnancy termination is a major public health globally in the highly important for maternal mortality. In this study, we have assessed cross-sectional data from Ethiopian Demographic and Health Survey to identify the occurrence of pregnancy termination and its risk factors from various women socioeconomic and demographic and characteristics.

Methods: The information collected from 15,683 women of the reproductive age group was considered in the study, and variables like maternal social and demographic characteristics were considered as risk factors of pregnancy termination. The study used descriptive statistics and binary logistic regression model to identify significant risk factors attributed to pregnancy termination.

Results: The descriptive statistics in the study revealed that out of total women aged 15-49 included in the study 7.9% were experienced pregnancy termination. Being illiterate (OR=1.773; 95% CI: (1.348, 2.331)) or only primary educational attainments (OR=1.462; 95% CI:(1.117,1.913)) younger age group (OR=1.359; 95% CI:(1.143, 1.617)) and lack of knowledge of contraceptive use were factors that are significantly associated with increased risk of pregnancy termination.

Conclusions: Maternal mortality should decline more rapidly to through reducing the rate of pregnancy termination achieve the Health Sector Transformation Plan target for maternal mortality in Ethiopia. Age education, knowledge about contraceptive methods use and occupation were the major significant factors associated with pregnancy termination. Thus, encouraging the use of contraceptives and improving women’s education is the most intervention to prevent the problem.

Abbreviations

CI: Confidence interval; DHS: Demographic and Health Surveys; ENDHS: Ethiopian Demographic and Health Surveys; CSA: Central Statistical Agency; LRT: Likelihood Ratio Test; OR: Odds ratios; SNNPR: South Nations Nationalities and Peoples Region; USAID: United States Agency for International Development; VIF: Variance Inflation Factor.

Background

Abortion is the most common problem in both developing and developed countries. It is estimated that 56 million pregnancy terminations are performed annually globally [1] and an estimated 42-63 women per every 100,000 abortions die every year. These abortion complicated maternal mortality due unsafe abortions accounts about 8-9% of all maternal deaths in the world. In different Africa regions annual rate abortion ranges from 31 per 1,000 women of reproductive age to in west Africa to 38 per 1000 in northern Africa [2]. Overall, 15% of all pregnancies in Africa were ends in abortion in the years 2010-2014 [1].

Previous studies showed that pregnancy termination not only harm the women, but also adverse birth outcomes such as prematurity (preterm birth), low birth weight, placental complications and birth defects exist as a consequence of pregnancy termination [3,4]. Rates of abortion related maternal deaths declining by 42% for the last 25 years since 1990 - 2014 from 108 maternal deaths per 100,000 abortions to 63 deaths per 100,000 abortions with the highest rate in Africa, 141 per 100,000 abortions [5,6]. About 9% of maternal deaths in Africa were due to unsafe abortion that resulted in the exclusive bleeding and infections [7].

Even though abortion rate in Ethiopia is lower compared to other East Africa countries, termination of pregnancy and the consequences are still major public health problems in Ethiopia. In Ethiopia Tessema et [8], at in their studies on the trends and cause of maternal death in Ethiopia during 1990-2013 found that, abortion is the second important cause of maternal mortality, accounting to 19.6% of maternal death following complications of anesthesia, embolism (air, amniotic fluid, and blood clot) and the condition of peripartum cardiomyopathy (25.7%). According to Sedgh G, et al [1], in Ethiopia, an estimated abortion is 24 per 1,000 women of reproductive age, while this is 34 per 1,000 women of reproductive age in the Eastern Africa region. In Ethiopia 38% of pregnancies were unintended, 47% of abortions were taking place in unsafe procedures and performed outside of health facilities. Among women undergo abortions outside of health facilities 40% were experiencing severe complications. The abortion rate was higher in urban areas, about 92 per 1,000 pregnancies [9].

The determinants of pregnancy termination are highly correlated with socioeconomic, demographic, behavioral factors of households and cultural factors. Several studies have investigated maternal age, and education is found to be strongly correlated with pregnancy termination [10-14]. Level of educational attainment is directly linked with pregnancy termination. Women of secondary and higher schooling were more likely to undergo an abortion [11,15-17]. Other determinants of pregnancy termination include maternal media exposure, contraceptive use, women’s autonomy, [10,18], marital status [19,20], birth order, parity [10] and religion [11]. Women who have been exposed to the media and who were contraceptive non-user more are likely to undergo an abortion. There is also a strong association between Occupation and abortion [10,20].

The main objective of this study is to assess the prevalence of pregnancy termination and the risk factors attributed to abortion among women of the reproductive age group in Ethiopia based on Ethiopian Demographic Health Survey, 2016 data sets.

Methods

This study used the Ethiopian Demographic and Health Surveys (EDHS) 2016 dataset which is freely available online [21] and contains information on a various population, health indicators such as, mothers’ and child health, use of family planning methods, nutritional status of women and children as well as household socioeconomic variables. The EDHS 2016 was conducted from January 18, 2016, to June 27, 2016, based on a nationwide representative sample that provides estimates at the national and regional levels and for urban and rural areas. The survey collects data on demographics, environmental, socioeconomic, family planning, infertility, nutritional and health status of children and their mothers from a nationally representative sample of 15,683 women aged 15–49 years and 12,688 men aged 15–59 years in 16,650 households.

Sampling

The survey used a two-stage cluster sampling technique. The country was stratified into nine regions and two administrative cities. The first stage involved selecting 645 enumeration areas (202 in urban areas and 443 in rural areas) with probability proportional to size. In the second stage, a predetermined 28 households per cluster were selected with an equal probability systematic selection from the newly created household list. The EDHS 2016 has three parts: the household questionnaire, the woman’s questionnaire and the man’s questionnaire. The data for occurrence of pregnancy termination and associated factors was taken from a woman’s survey. Data were collected by conducting face-to-face interviews with women who met the eligibility criteria (women aged 15–49 years). The study population in this study is all reproductive age group women (15-49 years) who had terminated a pregnancy five years preceding the survey those who responded to the women’s EDHS questionnaire in 2016.

Measurement of variables

Determinants of pregnancy termination status in this study were selected from the available similar studies on the subject; the main predictors explored for pregnancy termination will be grouped into Demographic and Socioeconomic characteristics related to mother and households. Variables included in this study are grouped into explanatory variables and dependent or response variable.

Dependent variable

During the conduct of the DHS 2016 each woman was asked to provide a detailed history if she has any miscarriages, abortions or stillbirths that ended before 2011.

The dependent variable of this study is abortion dichotomized as: “yes”, if a woman had undergone an abortion and “no” if the respondent had not undergone an abortion in the last five years. Thus, the response variable (pregnancy termination) of the of the ith woman Yi is a binary indicator variable of the response, such that

Y i ={ 1 if  the  i th  woman had undergone an abortion in the last five years             0 if  the  i th  woman  had not undergone an abortion in the last five years   MathType@MTEF@5@5@+=feaaguart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbnvMCYL2DLfgDOvMCaeXatLxBI9gBaerbd9wDYLwzYbItLDharuavP1wzZbItLDhis9wBH5garqqtubsr4rNCHbGeaGakY=ik0xXdbba91rFfpec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqaq=JfrVkFHe9pgea0dXdar=Jb9hs0dXdbPYxe9vr0=vr0=vqpWqaaeaabiGaciaacaqabeaadaabauaaaOqaaabaaaaaaaaapeGaamywa8aadaWgaaWcbaWdbiaadMgaa8aabeaak8qacqGH9aqpdaGabaWdaeaafaqabeGabaaabaWdbiaaigdacaGGGcGaamyAaiaadAgacaGGGcGaaiiOaiaadshacaWGObGaamyzaiaacckacaWGPbWdamaaCaaaleqabaWdbiaadshacaWGObaaaOGaaiiOaiaadEhacaWGVbGaamyBaiaadggacaWGUbGaaiiOaiaadIgacaWGHbGaamizaiaacckacaWG1bGaamOBaiaadsgacaWGLbGaamOCaiaadEgacaWGVbGaamOBaiaadwgacaGGGcGaamyyaiaad6gacaGGGcGaamyyaiaadkgacaWGVbGaamOCaiaadshacaWGPbGaam4Baiaad6gacaGGGcGaamyAaiaad6gacaGGGcGaamiDaiaadIgacaWGLbGaaiiOaiaadYgacaWGHbGaam4CaiaadshacaGGGcGaamOzaiaadMgacaWG2bGaamyzaiaacckacaWG5bGaamyzaiaadggacaWGYbGaam4CaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaiaacckacaGGGcGaaiiOaaWdaeaapeGaaGimaiaacckacaWGPbGaamOzaiaacckacaGGGcGaamiDaiaadIgacaWGLbGaaiiOaiaadMgapaWaaWbaaSqabeaapeGaamiDaiaadIgaaaGccaGGGcGaam4Daiaad+gacaWGTbGaamyyaiaad6gacaGGGcGaaiiOaiaadIgacaWGHbGaamizaiaacckacaWGUbGaam4BaiaadshacaGGGcGaamyDaiaad6gacaWGKbGaamyzaiaadkhacaWGNbGaam4Baiaad6gacaWGLbGaaiiOaiaadggacaWGUbGaaiiOaiaadggacaWGIbGaam4BaiaadkhacaWG0bGaamyAaiaad+gacaWGUbGaaiiOaiaadMgacaWGUbGaaiiOaiaadshacaWGObGaamyzaiaacckacaWGSbGaamyyaiaadohacaWG0bGaaiiOaiaadAgacaWGPbGaamODaiaadwgacaGGGcGaamyEaiaadwgacaWGHbGaamOCaiaadohacaGGGcGaaiiOaaaaaiaawUhaaaaa@DBA6@

Statistical Analysis

Descriptive characteristics of the subjects are presented as frequencies and percentages. To estimate the effect of socioeconomic and demographic factors on pregnancy termination (odds ratios with their 95% confidence intervals), regression logistic analysis was performed using Stata 15. Bivariate analysis based on Pearson chi square test was used for testing association between the predictors and outcome variable pregnancy termination. All significant predictor variables (p < 0.05) in the bivariate analysis were included in the multivariate logistic regression analysis. Multicollinearity between covariates was checked using the variance inflation factor (VIF) and VIF values greater than 10 indicates the existence of multicollinearity. The goodness of fit of the fitted models was checked using the likelihood ratio test (LRT).

Results

Descriptive Statistics

The descriptive statistics and the prevalence of pregnancy termination by socioeconomic and demographic characteristics of the women is presented in table 1. A total of 15,683 women of the reproductive age group was participating in this analysis. The prevalence of pregnancy termination five years before the survey period was 7.9% in this study population. The prevalence was higher among residents in Tigray region (10.3) followed by the Affar (9.5%), Amhara (9%) and Dire Dawa (9%). Concerning to place residence, the prevalence of pregnancy termination was consistent between rural and urban women. The five-year prevalence of pregnancy termination was lower among older age mothers 35 and older ages (6.5%) (Table 1).

The Pearson chi-square test result in table 1 revealed that among the covariates, region, women’s age, education level, religion, knowledge of contraceptive use, marital status and occupation were found to have a significant association with pregnancy termination at the 5 % level of significance (p-values < 0.05).

Results of binary logistic regression analysis

We start here first by checking the overall goodness of fit using the likelihood ratio tests (LRT). We then proceed to test the significance of each covariate included in the model. Accordingly, the likelihood ratio test based on chi-square distribution, provided a chi-square value of 1408.432 with (p-value < 0.0001), which would imply good fit for the model. Thus, the null hypothesis that there is no difference between the model with no covariate and the model with explanatory variables was rejected.

Concerning the regional disparity in pregnancy termination, the results in table 2 show that women in Amhara (OR: 0.72, 95% CI: 0.532, 0.972), Oromiya (OR: 0.663, 95% CI: 0.501, 0.876) , Benishangul-Gumuz (OR: 0.322, 95% CI: 0.222, 0.467), SNNPR (OR: 0.579, 95% CI: 0.422, 0.796), Gambella (OR: 0.373, 95% IC: 0.250, 0.557), Harari (OR: 0.42, 95% CI: 0.456, 0.903) are at a lower risk of experiencing abortion than women in Dire Dawa city administration. The study also reveals that women residing in the Addis Ababa city administration had the highest probability of pregnancy termination than women in Dire Dawa (OR = 1.408, 95% CI: 1.061, 1.869) (Table 2).

Risk of pregnancy termination decreases as women age increases. The risk of a woman to terminate pregnancy is higher among teenagers relative (OR=1.359, 95% CI: 1.143-1.617) to those 35 and higher years old (reference category). Similarly, women aged 20-24 years had about 40% higher chance of terminating pregnancy (OR=1.404, 95% CI: 1.171, 1.684) , and those aged 25-29 years old the prevalence of abortion was 34% higher compared to the reference age group 35 and higher (OR = 1.341, 95% CI: 1.115, 1.614). This figure was about 27% higher for those women 30-34 years old (OR = 1.272, 95% CI: 1.042, 1.554).

Educational attainment has a significant negative association with pregnancy termination. Women with no education are 1.77 times more likely to terminate pregnancy (OR = 1.773, 95% CI: 1.348, 2.331) and those with primary education have a 46% increased risk of terminating pregnancy (OR = 1.462, 95% CI: 1.117, 1.913), respectively, relative to women with higher education level. This finding reveals there is difference in pregnancy termination between women with secondary and higher education level.

The chance of abortion among women who had knowledge of contraceptive use as compared to those who had no knowledge of contraceptive use is 43% less likely. This means the risk pregnancy termination for women who had knowledge of contraceptive use is decreased more than by half compared to women who had no knowledge of contraceptive use (OR = 0569, 95% CI: 0.389, 0.832).

Marital status (single vs. married) is also a significant predictor of abortion. The 95% confidence interval suggests that the risk of abortion among single women is about 75% less likely compared those married women (OR = 0.252, 95% CI: 0.213, 0.298). The study also showed a significant association concerning the woman’s occupation. It was found that the probability of pregnancy termination was less likely to occur among unemployed women compared to the employed counterpart (OR = 0.619, 95% CI: 0.537, 0.715).

Discussion

The data for this study were extracted from the EDHS 2016, which is the nationwide survey conducted in Ethiopia using a nationally representative sample. The questionnaires used are comparable to the reference standard to measure external validity. The response rate (98.0%) of this study was very high compared to other national level surveys [22].

The five-year prevalence of pregnancy termination was 7.9%. Concerning geographical region Addis Ababa and Dire Dawa city administrations had the highest probability of pregnancy termination. In line with evidence from Ethiopian demography and health survey 2016 report, the proportion of pregnancy termination is higher among Addis Ababa and Dire Dawa city administrations. This is consistent with findings of [19,23,24], from Ethiopian demographic and health survey 2011.

Experiencing abortion had also associated with women’s knowledge of contraceptive use. Having knowledge of contraceptive use was a protective factor to reduce pregnancy termination. The odd of pregnancy termination is higher for women without knowledge of contraceptive use. Studies have shown that knowledge of contraceptive knowledge had significantly decreased the occurrence of unwanted pregnancies and abortion [25, 26]. This is because poor utilization of contraceptive methods is the basis of unintended pregnancy.

The findings of the study show that no difference in the risk of pregnancy termination between urban and rural residents, which is similar to finding of [11], from Nepal Demographic and Health Survey 2011. But some studies found higher abortion rates among urban women [27]. In this study household economic status had no significant impact on pregnancy termination. Several studies also identified wealth index is not associated with pregnancy termination [3,11], while many other studies found a significant correlation between pregnancy termination and wealth index [26,28,29].

The findings of this study also show that woman’s education level had inversely related to pregnancy termination. Educated women were less likely to undergo abortions and uneducated and women with only primary education were more likely to undergo an abortion. From various literatures and theories, illiterate women are significantly exposed to abortion [30-32]. This is due to the fact that educated women use modern contraceptive methods to protect unwanted pregnancy. Further, the odds of pregnancy termination among women of the younger age group were very high compared to those who were 35 and older. The finding revealed that this was consistent with reports from several studies previously conducted to examine the prevalence of pregnancy termination [25].

The study assessed the relation of marital status and occupation to pregnancy termination. Married women were less likely to terminate pregnancy. Some studies in literature also reported a significant association between marital status and abortion [27,33], in which the probability of pregnancy termination is higher among women who were single or not married. This study also found that the rate of pregnancy increases among employed women. The finding is in line with the study in literature [10,34], that revealed the likelihood of abortion is high among women who were employed.

Conclusions

The study found that pregnancy termination was significantly associated with the region, age, education, knowledge of contraceptive use, marital status and occupation at the 5% level of significance. Illiterate women or women with primary educational attainments, who had no knowledge of contraceptive use and those who were younger were more likely to terminate pregnancy. Therefore, reproductive health programs that focus on awareness creation of contraceptive methods use and family planning among women of the productive age group should give more attention to reduce pregnancy termination that is due to unwanted pregnancy. Furthermore, educating women who were illiterate would also be an important to decrease the burden maternal mortality as consequences pregnancy complications.

Limitations

The lower prevalence of pregnancy termination is this study might be due to unwillingness of women to report having had a pregnancy termination during the Ethiopia Demography and Health Survey or is a secret public health problem affecting the reproductive health outcome status of women.

We are grateful to “Measure DHS” for granting us permission to use the Demographic and Health Survey (DHS) data for this study.

Authors’ contributions

BT planned and prepared the overall manuscript, analyzed, interpreted and finalized the article.

  1. Sedgh G, Bearak J, Singh S, Bankole A, Popinchalk A, et al. (2016) Abortion incidence between 1990 and 2014: global, regional, and sebregional levels and trends. Lancet 388: 258-267. Link: http://bit.ly/2GI4HdT
  2. Singh S, Remez L, Sedgh G, Kwok L, Onda T, Abortion Worldwide 2017: Uneven Progres and Unequal Access, New York: Guttmacher Institute. Link: http://bit.ly/2KjOsF1  
  3. Chae S, Desai S, Crowell M, Sedgh G, Singh S (2017) Characteristics of women obtaining induced abortions in selected low- and middle-income countries. PLoS One 12: e0172976.  Link: http://bit.ly/2OymdaV
  4. Bhattacharya S, Lowit A, Bhattacharya S, Raja EA, Lee AJ, et al. (2012) Reproductive outcomes following induced abortion: a national register-based cohort study in Scotland. BMJ Open 2: pii: e000911. Link: http://bit.ly/2YAKNM3
  5. Starrs AM, Ezeh AC, Barker G, Basu A, Bertrand JT, et al. (2018) Accelerate Progress sexual and reproductive health and rights for all: report of the Guttmacher Lancet commission. Lancet 391: 2642-2692. Link: http://bit.ly/2YihqPi
  6. Say L, Chou D, Gemmill A, Tunçalp Ö, Moller AB, et al. (2014) Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health 2: e323-e333. Link: http://bit.ly/2ZnXHug
  7. Singh S, Darroch JE, Ashford LS (2014) Adding IT Up: The Costs and Benefits of Investing in Sexual and Reproductive Health. New York: Guttmacher Institute. Link: http://bit.ly/2LVW7MT  
  8. Gizachew Assefa Tessema, Caroline O Laurence, Yohannes Adama Melaku, Awoke Misganaw, Sintayehu A Woldie (2017) Trends and causes of maternal mortality in Ethiopia during 1990-2013: findings from the Global Burden of Diseases study 2013. BMC public health 17: 160. Link: http://bit.ly/335VTbv  
  9. Ann M Moore, Yirgu Gebrehiwot, Tamara Fetters, Yohannes Dibaba Wado, Akinrinola Bankole, et al. (2016) The estimated incidence of induced abortion in ethiopia, 2014: changes in the provision of services since 2008. Int Perspect Sex Reprod Health 42: 111-120. Link: http://bit.ly/319TMBG
  10. Bago BJ, Hibstu DT, Woldemariam SH (2017) Prevalence of Pregnancy Termination and Its Associated Factors Among Women of Reproductive Age Group in Ethiopia Using 2011 Ethiopian Demography and Health Survey, 2016. J Preg Child Health 4: 359. Link: http://bit.ly/336tcva  
  11. Yogi A, K C P, Neupane S (2018) Prevalence and factors associated with abortion and unsafe abortion in Nepal: a nationwide cross-sectional study. BMC Pregnancy and Childbirth 18: 376. Link: http://bit.ly/2YDMCIf
  12. Ilboudo P, Somda S, Sundby J (2014) Key determinants of induced abortion in women seeking post abortion care in hospital facilities in Ouagadougou, Burkina Faso. Int J Womens Health 6: 565-2. Link: http://bit.ly/2Myl7Jz
  13. Maina BW, Mutua MM, Sidze EM (2015) Factors associated with repeat induced abortion in Kenya. BMC Public Health. 15: 1048. Link: http://bit.ly/2Mx5D8J
  14. Gao GP, Zhang RJ, Zhang XJ, Jia XM, Li XD, et al. (2015) Prevalence and associated factors of induced abortion among rural married women: a cross-sectional survey in Anhui, China. J Obstet Gynaecol Res 41: 383-1. Link: http://bit.ly/2KaoS6o
  15. Tamang A, Tuladhar S, Tamang J, Ganatra B, Dulal B (2012) Factors associated with choice of medical or surgical abortion among women in Nepal. Int J Gynaecol Obstet 118: S52–6. Link: http://bit.ly/2YnkObK
  16. Sundaram A, Juarez F, Bankole A, Singh S (2012) Factors associated with abortionseeking and obtaining a safe abortion in Ghana. Stud Fam Plan 43: 273-86. Link: http://bit.ly/2Yl7DYL
  17. Mote CV, Otupiri E, Hindin MJ (2010) Factors associated with induced abortion  mong women in Hohoe. Ghana. Afr j Reprod Health 14: 110-6. Link: http://bit.ly/31eyUcj
  18. Adino Tesfahun Tsegaye, Menderie Mengistu, Alemayehu Shimeka (2018) Prevalence of unintended pregnancy and associated factors among married women in west Belessa Woreda, Northwest Ethiopia, 2016 Reproductive Health  15: 201 Link: http://bit.ly/336vCtK
  19. Mulatu T, Cherie A, Negesa L (2017) Prevalence of Unwanted Pregnancy and Associated Factors among Women in Reproductive Age Groups at Selected Health Facilities in Addis Ababa, Ethiopia. J Women's Health Care 6: 392. Link: http://bit.ly/2YFgik4
  20. Diriba Gebeyehu, Bitiya Admassu, Makida Sinega, Merga Haile (2015) Assessment of Prevalence and Reasons for Termination of Pregnancy at Jimma University Teaching Hospital, Ethiopia. Universal Journal of Public Health 3: 251-255. Link: http://bit.ly/2KiMW5V  
  21. Measure DHS. The DHS program: demographic and health surveys. Rockville: ICF Macro.
  22. Central Statistical Agency (CSA) [Ethiopia] and ICF (2016) Ethiopia Demographic and Health SurveyLink: http://bit.ly/2T3qzFB
  23. Singh S, Fetters T, Gebreselassie H, Abdella A, Gebrehiwot Y, et al. (2010) The Estimated Incidence of Induced Abortion In Ethiopia,2008. International Perspectives on Sexual and Reproductive Health, 36: 16‐25. Link: http://bit.ly/2SYpwXF
  24. (2010) Facts on Unintended Pregnancy and Abortion in Ethiopia. Guttmacher 2. Link: http://bit.ly/2yxZGQU
  25. Yaya S, Amouzou A, Uthman OA, Ekholuenetale M, Bishwajit G, et al. (2018) Prevalence and determinants of terminated and unintended pregnancies among married women: analysis of pooled cross-sectional surveys in Nigeria. BMJ Glob Health 3: e000707. Link: http://bit.ly/2LYHys2
  26. Habte D, Teklu S, Melese T, Mgaywa GMD Magafu (2013) Correlates of unintended pregnancy in Ethiopia: results from a national survey. PLoS One8: e82987. Link: http://bit.ly/2Ka4q5G
  27. Puri M, Singh S, Sundaram A, Hussain R, Tamang A, (2016) Abortion Incidence and Unintended Pregnancy in Nepal. International perspectives on sexual and reproductive health, 42:197-209. Link: http://bit.ly/2LVf02z
  28. Pallitto CC, García-Moreno C, Jansen HA, Heise L, Ellsberg M, et al. (2013) Intimate partner violence, abortion, and unintended pregnancy: results from the WHO Multi-country Study on Women’s Health and Domestic Violence. Int J Gynaecol Obstet 120: 3-9. Link: http://bit.ly/2Myp5lG
  29. Gite A, Liulseged N, Seyife H, Abrha Y, Workineh Y, et al. (2016) Unintended Pregnancy: Magnitude and Associated Factors among Pregnant Women in Arba Minch Town, Gamo Gofa Zone, Ethiopia 2015. Reprod Syst Sex Disord5: 2-6. Link: http://bit.ly/2ZrLkgv
  30. Gebreamlak W, Aragaw A, Lemma S, Demilew W (2014) Magnitude and factors influencing unintended pregnancy among pregnant women attending antenatal care at Felege Hiwot referral hospital, Northwest Ethiopia: A crosssectional study. Science Journal of Public Health 2: 261-269. Link: http://bit.ly/2GFY382
  31. Teshome FT, Hailu AG, Teklehayma AN (2014) Prevalence of unintended pregnancy and associated factors among married pregnant women in Ganji woreda,west Wollega Oromia region, Ethiopia. Science Journal of Public Health 2: 92-101. Link: http://bit.ly/2OAoLpf
  32. Geda NR, Lako TK (2011) A population‐based study on unintended pregnancy among married women in a district in Southern Ethiopia. Journal of Geography and Regional Planning 4: 417‐427. Link: http://bit.ly/2Zo8Hb2
  33. Kathryn L Andersen, Ram Chandra Khanal, Alexandra Teixeira, Shailes Neupane, Sharad Sharma (2015) Marital status and abortion among young women in Rupandehi, Nepal. BMC Women's Health 15:17. Link: http://bit.ly/2KjPNM1    
  34. Tadele Kassie, Geteneh Moge, Ahmed Al, Worku Tefera (2017)Magnitude and factors associated with unintended pregnancy among pregnant women in Addis Ababa, Ethiopia. Global journal of medicine and public health 6: 4. Link: http://bit.ly/2KispyE
© 2019 Woldeamanuel BT. This is an open-ijsrhccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.