Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some start antenatal care late during the pregnancy.In Kenya, total fertility rate has decreased for the last three decades from 8.1 to 3.9. However, with the decrease of total fertility rate, prevalence of maternal mortality and morbidity factors has greatly impacted on the pregnancy. Among them is spontaneous abortion. This study used secondary data from Kenyatta national hospital and employed logistic regression to model miscarriage's risk factors, investigate socio demographic and lifestyle factors, to investigate interactions among identified risk factors and fit a predictive model. Significant socio demographic factors identified were age and recurrent miscarriage. A woman who had experienced prior miscarriage had a 7.5-fold risk. Lifestyle factors identified were body mass index, diabetes mellitus and HIV. Underweight women had a 13.2-fold risk. There were significant interactions between gravidity and previous miscarriage; diabetes and body mass index. A predictive model was fit. The model has a good measure of separability, 80% classification accuracy and it is significant.
| Published in | International Journal of Data Science and Analysis (Volume 5, Issue 6) |
| DOI | 10.11648/j.ijdsa.20190506.16 |
| Page(s) | 143-147 |
| 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. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
Spontaneous Abortion, Logistic Regression, Risk Factor
| [1] | Akgül A. and Çevik O., 2003, Statistical Analysis Methods “Management Implementation is SPSS”, Emek Offset, Ankara. |
| [2] | Arck, P. C., Rucke, M., Rose, M., Szekeres-Bartho, J., Douglas, A. J., Pritsch, M., Blois, S. M., Pincus, M. K., Barenstrauch, N., Dudenhausen, J. W., et al. (2008). Early risk factors for miscarriage: a prospective cohort study in pregnant women. Reproductive biomedicine online, 17 (1): 101-113. |
| [3] | Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39 (227): 357-365. |
| [4] | Callander, G., Brown, G. P., Tata, P., and Regan, L. (2007). Counterfactual thinking and psychological distress following recurrent miscarriage. Journal of Reproductive and Infant Psychology, 25 (1): 51-65. |
| [5] | Cox, D. (1970). The analysis of binary data, Methuen & co. Ltd., London, pages 48-52. |
| [6] | Cramer, D. W. and Wise, L. A. (2000). The epidemiology of recurrent pregnancy loss. In Seminars in reproductive medicine, volume 18, pages 331-340. |
| [7] | Dietz K., Gail M., Krickeberg K., Samet J. and Tsiatis A., (2005). Regression Methods in Biostatistics; Linear, Logistic, Survival, and Repeated Measures Models. Statistics for Biology and Health. ISBN 0-387-20275-7. |
| [8] | Goh, J. Y., He, S., Allen, J. C., Malhotra, R., and Tan, T. C. (2016). Maternal obesity is associated with a low serum progesterone level in early pregnancy. Hormone molecular biology and clinical investigation, 27 (3): 97-100. |
| [9] | Hassold, T. and Hunt, P. (2001). To err (meiotically) is human: the genesis of human aneuploidy. Nature Reviews Genetics, 2 (4): 280. |
| [10] | Kouk, L. J., Neo, G. H., Malhotra, R., Allen, J. C., Beh, S. T., Tan, T. C., and Ostbye, T. (2013). A prospective study of risk factors for first trimester miscarriage in Asian women with threatened miscarriage. Singapore Med J, 54 (8): 425-431. |
| [11] | Lim, T.-S., Loh, W.-Y., and Shih, Y.-S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine learning, 40 (3): 203-228. |
| [12] | Metwally, M., Saravelos, S. H., Ledger, W. L., and Li, T. C. (2010). Body mass index and risk of miscarriage in women with recurrent miscarriage. Fertility and sterility, 94 (1): 290-295. |
| [13] | Otieno, G., Okanda, J., Kinuthia, J., John-Stewart, G., Akelo, V., and Kohler, P. (2018). Prior miscarriage prevalence higher among hiv positive than in hiv-negative women: A community survey in rural kenya. J PediatrWomens Healthcare. 2018; 1 (2), 1011. |
| [14] | Regan, L. and Rai, R. (2000). Epidemiology and the medical causes of miscarriage. Best practice & research Clinical obstetrics &gynaecology, 14 (5): 839-854. |
| [15] | Sugiura-Ogasawara, M., Ozaki, Y., Kitaori, T., Suzumori, N., Obayashi, S., and Suzuki, S. (2009). Live birth rate according to maternal age and previous number of recurrent miscarriages. American Journal of Reproductive Immunology, 62 (5): 314-319. |
| [16] | Warakamin, S., Boonthai, N., and Tangcharoensathien, V. (2004). Induced abortion in Thailand: current situation in public hospitals and legal perspectives. Reproductive Health Matters, 12 (sup24): 147-156. |
APA Style
Edwin Kung’u Kagereki, Anthony Wanjoya, Thomas Mageto. (2019). Modelling Cases of Spontaneous Abortion Using Logistic Regression. International Journal of Data Science and Analysis, 5(6), 143-147. https://doi.org/10.11648/j.ijdsa.20190506.16
ACS Style
Edwin Kung’u Kagereki; Anthony Wanjoya; Thomas Mageto. Modelling Cases of Spontaneous Abortion Using Logistic Regression. Int. J. Data Sci. Anal. 2019, 5(6), 143-147. doi: 10.11648/j.ijdsa.20190506.16
AMA Style
Edwin Kung’u Kagereki, Anthony Wanjoya, Thomas Mageto. Modelling Cases of Spontaneous Abortion Using Logistic Regression. Int J Data Sci Anal. 2019;5(6):143-147. doi: 10.11648/j.ijdsa.20190506.16
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Download@article{10.11648/j.ijdsa.20190506.16,
author = {Edwin Kung’u Kagereki and Anthony Wanjoya and Thomas Mageto},
title = {Modelling Cases of Spontaneous Abortion Using Logistic Regression},
journal = {International Journal of Data Science and Analysis},
volume = {5},
number = {6},
pages = {143-147},
doi = {10.11648/j.ijdsa.20190506.16},
url = {https://doi.org/10.11648/j.ijdsa.20190506.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190506.16},
abstract = {Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some start antenatal care late during the pregnancy.In Kenya, total fertility rate has decreased for the last three decades from 8.1 to 3.9. However, with the decrease of total fertility rate, prevalence of maternal mortality and morbidity factors has greatly impacted on the pregnancy. Among them is spontaneous abortion. This study used secondary data from Kenyatta national hospital and employed logistic regression to model miscarriage's risk factors, investigate socio demographic and lifestyle factors, to investigate interactions among identified risk factors and fit a predictive model. Significant socio demographic factors identified were age and recurrent miscarriage. A woman who had experienced prior miscarriage had a 7.5-fold risk. Lifestyle factors identified were body mass index, diabetes mellitus and HIV. Underweight women had a 13.2-fold risk. There were significant interactions between gravidity and previous miscarriage; diabetes and body mass index. A predictive model was fit. The model has a good measure of separability, 80% classification accuracy and it is significant.},
year = {2019}
}
TY - JOUR T1 - Modelling Cases of Spontaneous Abortion Using Logistic Regression AU - Edwin Kung’u Kagereki AU - Anthony Wanjoya AU - Thomas Mageto Y1 - 2019/12/25 PY - 2019 N1 - https://doi.org/10.11648/j.ijdsa.20190506.16 DO - 10.11648/j.ijdsa.20190506.16 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 143 EP - 147 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20190506.16 AB - Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some start antenatal care late during the pregnancy.In Kenya, total fertility rate has decreased for the last three decades from 8.1 to 3.9. However, with the decrease of total fertility rate, prevalence of maternal mortality and morbidity factors has greatly impacted on the pregnancy. Among them is spontaneous abortion. This study used secondary data from Kenyatta national hospital and employed logistic regression to model miscarriage's risk factors, investigate socio demographic and lifestyle factors, to investigate interactions among identified risk factors and fit a predictive model. Significant socio demographic factors identified were age and recurrent miscarriage. A woman who had experienced prior miscarriage had a 7.5-fold risk. Lifestyle factors identified were body mass index, diabetes mellitus and HIV. Underweight women had a 13.2-fold risk. There were significant interactions between gravidity and previous miscarriage; diabetes and body mass index. A predictive model was fit. The model has a good measure of separability, 80% classification accuracy and it is significant. VL - 5 IS - 6 ER -
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