Background: Clinical trials for COVID-19 vaccines initially excluded pregnant women. However, observational studies revealed a relative safety of the vaccine during pregnancy therefore association between different types of COVID-19 vaccination and the risk of abortion must be studied.Objectives: The objective is to explore the possible association between abortion and different types of COVID-19 vaccination in Jeddah.Methods: This was a retrospective cross-sectional study done in three private general hospitals in Jeddah using electronic medical records and phone interviews of pregnant women who were admitted with abortion. Women were then interviewed for their vaccination data (type, dose) and their current pregnancy outcome (aborted or not).Results: Medical records of 214 women diagnosed with abortion were included; 13.1% of them managed to continue their pregnancy. Vaccinated women (86%) had significantly earlier gestational age (p=0.031), higher hypertension (<0.001), and lower positive consanguinity (<0.001) compared to non-vaccinated women. The type (p=0.636) and number (p=0.331) of vaccination did not differ significantly among vaccinated women with and without abortion. Significant predictors of abortion were age>35 years (OR: 3.1, 95% CI: 1.34-6.97, p=0.008), diabetes (OR: 0.09, 95% CI: 0.01-0.89, p=0.040), and positive consanguinity (OR: 0.12, 95% CI: 0.02-0.63, p=0.012). However, spontaneous abortion did not have an increased odds of exposure to COVID-19 vaccines (OR: 1.07, 95% CI: 0.21-5.49, p=0.937).Conclusion: COVID-19 vaccination is not associated with an increased risk of abortion in women vaccinated during their first or second trimesters. Further clinical trials are needed to support the evidence of the safety of early vaccination of pregnant women.

We propose a novel lifetime model by extending the new exponential-gamma distribution to the exponentiated new exponential-gamma distribution. This extension allows for the derivation of a more flexible density function that combines the characteristics of the exponential and gamma distributions. We present various statistical properties of the newly proposed method, including the cumulative function, probability density function, moment-generating function, and moments. Additionally, we discuss the estimation of parameters using maximum likelihood. To compare the performance of our newly developed model with existing probability distributions (gamma, exponential, Lindley, generalized gamma, generalization of the generalized gamma, and new exponential-gamma distribution), we employ model selection criteria such as the Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICC), and the Bayesian Information Criterion (BIC). The application of these criteria to different models demonstrates that our proposed model outperforms the other six models across various datasets. For instance, in the first dataset, the AIC, AICC, and BIC values for our model are 366.975, 373.805, and 373.805, respectively, whereas the values for the other six models (exponential, Lindley, generalized gamma, generalization of the generalized gamma) range from 503.012 to 834.327. We conduct simulation studies to assess the efficiency of our proposed model. Furthermore, we apply the proposed method to three real data applications to further examine its effectiveness. It is important to note that the quantile function of the proposed model does not have a closed-form solution, requiring the computation of the quantile function through the Newton-Raphson iterative approach.

Various statistical distributions are still being used extensively over the previous decades for modeling data in numerous areas such as engineering, sciences, and finance. Nonetheless, in a lot of applied areas, there is a continuous need for expanded forms of these distributions. However, many common distributions do not fit the data well. Thus, new distributions have been constructed in literature. The purpose of this article is to present a new family of distributions using the Dagum distribution as a generator and to study its properties such as hazard rate functions, moments, quantile function, ordered statistics and Renyi entropy. Moreover, one sub model called Dagum-Frechet distribution is discussed with some of its properties. The maximum likelihood estimation is employed to estimate the parameters of the proposed distribution, and the confidence intervals are obtained. Finally, two real data sets are analyzed to illustrate the performance of the purposed distribution.

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