Adverse pregnancy outcome is a complex outcome of pregnancy other than the normal live birth. It lead to serious health consequences to the mother or the baby. It also can be still major public health and socioeconomic status problems in developing countries where most pregnancies are unplanned, complications. There is disparity of adverse pregnancy outcomes rate from region to region in Ethiopia. Objectives: The main objectives of the study were to identify the important determinant of adverse pregnancy outcomes in Ethiopia. With this study the multilevel logistic regression models were used to explore the major risk factors and regional variations. Different stages of multilevel models like intercept model and slope model were employed to attain the goal of the study. The results indicated that out of 15683 reproductive age of women, 8412 (86.8%) not experiencing adverse pregnancy outcome while 1282 (13.2%) of women have experienced adverse pregnancy outcome at the time of the survey. From multilevel logistic regression, it was found that the random intercept model provided the best fit for the data under consideration. All the fitted models gave the same conclusion that, Age of mother, place of residence, antenatal care visit and delivery place, Parity, Education of mother, Marital status, Anemia level were found to be statistically significant. Conclusion: The random intercept multilevel model provided the best fit for the data under consideration. Furthermore, it is found that not having Antenatal care, residing in rural area, working occupational status, being anemic, increased educational level, never married, divorced, or separated marital status, being in age group of 15-24 or >35 years are associated with increased risk of adverse pregnancy outcome among reproductive age group women in Ethiopia.
| Published in | American Journal of Health Research (Volume 9, Issue 2) |
| DOI | 10.11648/j.ajhr.20210902.15 |
| Page(s) | 57-63 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Adverse Pregnancy Outcome, Abortion, Multilevel Logistic Regression Model
| [1] | World health organization (2015) Maternal and child health. |
| [2] | World Health Organization (2001) Definitions and indicators in family planning and maternal and child health and Reproductive health. European Regional Office World Health Organization. |
| [3] | Lawn, Blencowe and Waiswa (2016) ‘For The Lancet Ending Preventable Stillbirths Series study group with The Lancet Stillbirth Epidemiology investigator group. Stillbirths: rates, risk factors, and acceleration towards 2030’, Lancet, 6736 (15), pp. 837–5. Available at: http://dx.doi.org/10.1016/S0140-. |
| [4] | Levandowski, B. A. et al. (2012) ‘Reproductive health characteristics of young Malawian women seeking post-abortion care. African Journal of Reproductive Health, 16 (2), 253-261’. |
| [5] | Francome, C. (2004) ‘Abortion in the USA and the UK. Ashgate Publishing, Surrey, 2004’. |
| [6] | Moore, A. M. et al. (2016) ‘of Services Since 2008 The Estimated Incidence of Induced Abortion in Ethiopia, 2014: Changes in the Provision of Services Since 2008’, International persp on sexual and repro health, 42 (3), pp. 111–120. doi: 10.1363/42e1816’. |
| [7] | Sedgh, G. et al. (2016) ‘Abortion incidence between 1990 and 2014: global, regional, and subregional levels and trends’, Lancet 2016, 388, pp. 258–67. doi: 10.1016/S0140-6736(16)30380-4’. |
| [8] | Say, Chou and Gemmill (2014) ‘Global causes of maternal death: a WHO systematic analysis’, Lancet Glob Health, 2, p. e323–33’. |
| [9] | Snijders, T. and Bosker, R. (1999) ‘Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modeling. London/ Thousand Oaks/ New Delhi: Sage Publications’. |
| [10] | Suesse, T. F. and & Liu, I. (2011) ‘Modelling Strategies for Repeated Multiple Response Data, Centre for Satatistical and Survey Methodology, University of Wollongong, Working Paper 04-11. 37p’. |
| [11] | Hox, J. J. (2010) Multilevel Analysis Techniques and Applications. 2nd Ed. New York. Utrecht University. Routledge. |
| [12] | Gebremeskel, F. et al. (2017) ‘Determinants of Adverse Birth Outcome among Mothers who Gave Birth at Hospitals in Gamo Gofa Zone, Southern Ethiopia: A Facility Based Case Control Study’, Quality in Primary Care, 25 (5), pp. 259–266’. |
| [13] | Gershim, A. et al. (2015) ‘Adverse pregnancy outcomes in rural Uganda (1996–2013): trends and associated factors from serial cross sectional survey’, BMC prenancy childbirth, 15, p. 279. doi: 10.1186/s12884-015-0708-8’. |
| [14] | Kenny, L. C. et al. (2013) ‘Advanced Maternal Age and Adverse Pregnancy Outcomes: Evidence from a large contemporary cohort. PLoS ONE 8 (2): e56583’. |
| [15] | Analizi, Kidanemariam and Habtamu (2017) ‘Multilevel Logistic Regression Analysis of the Determinants of Stillbirth in Ethiopia Using EDHS 2011 Data’, Turkiye Klinikleri J Biostat, 9 (2), pp. 121–142. doi: 10.5336/biostatic.2016-54437. |
| [16] | EsheteA. (2013) ‘Birth outcomes among laboring mothers in selected health facilities of North Wollo Zone A. Eshete et al./ Health 5: 1141-1150’. |
| [17] | Magadi, M. A., Diamond, I. and Madise, and N. (2004) ‘Analysis of factors associated with maternal mortality in Kenyan hospitals II Journal of Biosocial Science 33: 375-389’. |
| [18] | Kalilani-Phiri, L. et al. (2015) ‘The severity of abortion complications in Malawi. International Journal of Gynecology and Obstetrics, 128, 160-164’. |
| [19] | OliverasE. et al. (2008) ‘Clinic based surveillance of adverse pregnancy outcomes to identify induced abortions in Accra, Ghana. Stud Fam Plann. 2008; 29: 133–140’. |
| [20] | Xiong, X. et al. (2000) ‘Anemia during pregnancy and birth outcome: a meta-analysis. Am J Perinatol. 2000; 17 (3): 137–46. View Article PubMed Google Scholar’. |
APA Style
Endale Alemayehu, Tsigereda Tilahun. (2021). Cross-regional Variation of Complex Pregnancy Problems in Ethiopia. American Journal of Health Research, 9(2), 57-63. https://doi.org/10.11648/j.ajhr.20210902.15
ACS Style
Endale Alemayehu; Tsigereda Tilahun. Cross-regional Variation of Complex Pregnancy Problems in Ethiopia. Am. J. Health Res. 2021, 9(2), 57-63. doi: 10.11648/j.ajhr.20210902.15
AMA Style
Endale Alemayehu, Tsigereda Tilahun. Cross-regional Variation of Complex Pregnancy Problems in Ethiopia. Am J Health Res. 2021;9(2):57-63. doi: 10.11648/j.ajhr.20210902.15
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajhr.20210902.15,
author = {Endale Alemayehu and Tsigereda Tilahun},
title = {Cross-regional Variation of Complex Pregnancy Problems in Ethiopia},
journal = {American Journal of Health Research},
volume = {9},
number = {2},
pages = {57-63},
doi = {10.11648/j.ajhr.20210902.15},
url = {https://doi.org/10.11648/j.ajhr.20210902.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20210902.15},
abstract = {Adverse pregnancy outcome is a complex outcome of pregnancy other than the normal live birth. It lead to serious health consequences to the mother or the baby. It also can be still major public health and socioeconomic status problems in developing countries where most pregnancies are unplanned, complications. There is disparity of adverse pregnancy outcomes rate from region to region in Ethiopia. Objectives: The main objectives of the study were to identify the important determinant of adverse pregnancy outcomes in Ethiopia. With this study the multilevel logistic regression models were used to explore the major risk factors and regional variations. Different stages of multilevel models like intercept model and slope model were employed to attain the goal of the study. The results indicated that out of 15683 reproductive age of women, 8412 (86.8%) not experiencing adverse pregnancy outcome while 1282 (13.2%) of women have experienced adverse pregnancy outcome at the time of the survey. From multilevel logistic regression, it was found that the random intercept model provided the best fit for the data under consideration. All the fitted models gave the same conclusion that, Age of mother, place of residence, antenatal care visit and delivery place, Parity, Education of mother, Marital status, Anemia level were found to be statistically significant. Conclusion: The random intercept multilevel model provided the best fit for the data under consideration. Furthermore, it is found that not having Antenatal care, residing in rural area, working occupational status, being anemic, increased educational level, never married, divorced, or separated marital status, being in age group of 15-24 or >35 years are associated with increased risk of adverse pregnancy outcome among reproductive age group women in Ethiopia.},
year = {2021}
}
TY - JOUR T1 - Cross-regional Variation of Complex Pregnancy Problems in Ethiopia AU - Endale Alemayehu AU - Tsigereda Tilahun Y1 - 2021/05/14 PY - 2021 N1 - https://doi.org/10.11648/j.ajhr.20210902.15 DO - 10.11648/j.ajhr.20210902.15 T2 - American Journal of Health Research JF - American Journal of Health Research JO - American Journal of Health Research SP - 57 EP - 63 PB - Science Publishing Group SN - 2330-8796 UR - https://doi.org/10.11648/j.ajhr.20210902.15 AB - Adverse pregnancy outcome is a complex outcome of pregnancy other than the normal live birth. It lead to serious health consequences to the mother or the baby. It also can be still major public health and socioeconomic status problems in developing countries where most pregnancies are unplanned, complications. There is disparity of adverse pregnancy outcomes rate from region to region in Ethiopia. Objectives: The main objectives of the study were to identify the important determinant of adverse pregnancy outcomes in Ethiopia. With this study the multilevel logistic regression models were used to explore the major risk factors and regional variations. Different stages of multilevel models like intercept model and slope model were employed to attain the goal of the study. The results indicated that out of 15683 reproductive age of women, 8412 (86.8%) not experiencing adverse pregnancy outcome while 1282 (13.2%) of women have experienced adverse pregnancy outcome at the time of the survey. From multilevel logistic regression, it was found that the random intercept model provided the best fit for the data under consideration. All the fitted models gave the same conclusion that, Age of mother, place of residence, antenatal care visit and delivery place, Parity, Education of mother, Marital status, Anemia level were found to be statistically significant. Conclusion: The random intercept multilevel model provided the best fit for the data under consideration. Furthermore, it is found that not having Antenatal care, residing in rural area, working occupational status, being anemic, increased educational level, never married, divorced, or separated marital status, being in age group of 15-24 or >35 years are associated with increased risk of adverse pregnancy outcome among reproductive age group women in Ethiopia. VL - 9 IS - 2 ER -
Email: