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Abstract Background: The U.N. health and well-being goals for 2030 focus on maternal and child health outcomes, among others. Challenges to meeting those goals vary widely throughout Nepal owing to the range of sociocultural factors, infrastructural limitations, physical geography and altitudes. This article explores sociocultural and biological influences on fertility and child survival among ethnically Tibetan women in Nepal. Methods: This is a cross sectional study of 430 women, age 46-86 years old, citizens of Nepal and native residents above 3500m in Mustang District, who provided interview and physiological data. Univariate Poisson regression analyses selected significant variables to include in multivariate Poisson regressions investigating the number of pregnancies, livebirths, child survival and death outcomes. Results: Earlier age at first pregnancy, later age at last pregnancy, and miscarriages associated with more pregnancies. Miscarriages and stillbirths associated with fewer livebirths. Higher maternal BMI and FEV6 associated with fewer children dying before age 15. Marital characteristics (status, type, continuity), contraceptive use, relative wealth, and education influenced these covariates. Conclusions: Low maternal pulmonary function and nutritional status predict poorer child survival in Upper Mustang. Addressing poor lung function and nutrition may improve reproductive outcomes among ethnically Tibetan women living at high altitude. Keywords: Child survival; fertility; high-altitude; Nepal; women..
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Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records
Abstract The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /− 0.01%. A parsimonious predictive model utilizing age (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /− 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.
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- PAR ID:
- 10361750
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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