outcomes of high-risk pregnancies in northern iran: multivariate logistic regression model
Authors
abstract
abstract background and purpose: high-risk pregnancy is referred to a situation in which mother, fetus or neonate are in higher risk of morbidity or mortality. because of adverse outcomes of high-risk pregnancies, this study aims to determine these outcomes in the north of iran. materials and methods: we recruited 803 urban and rural pregnant women in this crosssectional study via consensus method. data were collected by a questionnaire and analyzed using descriptive statistics [mean, standard deviation (sd)], chi-square test and multivariate logistic regression model. all data analyses were performed using spss software and p < 0.05 was considered significant. results: mean ± sd, minimum and maximum age of participants were 27.0 ± 6.2, 14 and 44 years, respectively, 26.3% of which were urban residences. the frequency of adverse outcomes of pregnancy (stillbirth, abortion, and weight under 2500 g) was 10.8%. according to the multivariate logistic regression model, preeclampsia was significantly associated with adverse outcomes of high-risk pregnancy (odds ratio = 2.7, 95% confidence interval: 1.03-7.10). conclusion: our study showed that preeclampsia during pregnancy is a predictive factor of adverse outcomes of pregnancy such as abortion, stillbirth, and low birth weight.
similar resources
Outcomes of High-Risk Pregnancies in Northern Iran: Multivariate Logistic Regression Model
Abstract Background and purpose: High-risk pregnancy is referred to a situation in which mother, fetus or neonate are in higher risk of morbidity or mortality. Because of adverse outcomes of high-risk pregnancies, this study aims to determine these outcomes in the North of Iran. Materials and Methods: We recruited 803 urban and rural pregnant women in this crosssectional ...
full textBayesian multivariate logistic regression.
Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...
full textPredictive factors for loneliness in female high school students; an unvariate and multivariate logistic regression analysis
Background and aims: Loneliness typically includes anxious feelings. It is particularly relevant to adolescence period. It has effect on physical and mental health. The present study aimed to identify the predictive factors of loneliness among high schools female students. Methods: A cross– sectional survey was carried out among high schools female students in Ilam during the academic year 201...
full textPrediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis
Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods : In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were ...
full textThe Relationship between Modified Biophysical Profile, Standard Biophysical Profile, and Neonatal Outcomes of High-risk Pregnancies
Introduction: High-risk pregnancies can result in many complications for the fetus. In these pregnancies, different tests such as non-stress test (NST), biophysical profile (BPP), oxytocin contraction stress test (OCT), and Doppler sonography can be used to evaluate fetal health. As standard BBP requires more time and expertise, in this study, we evaluated the relationship between standard BPP,...
full textMy Resources
Save resource for easier access later
Journal title:
iranian journal of health sciencesجلد ۳، شماره ۴، صفحات ۴۰-۴۶
Keywords
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023