Prevalence and predictive factors for infertility-related stress among infertile couples: A cross-sectional study from Jordan and the occupied Palestinian territories

Risk of Infertility in Reproductive-Age Patients With Thyroid Cancer Receiving or Not Receiving 131I Treatment: A Nationwide Population-Based Cohort Study

BACKGROUND

Fertility is the crucial concern for many survivors of cancer diagnosed as children, adolescents, and young adults. The objective of this study was to determine the risk of infertility in reproductive-age patients with thyroid cancer receiving or not receiving radioiodine treatment in Taiwan.

METHODS

This nationwide population-based cohort study was conducted using data obtained from the Taiwan National Health Insurance Database from 2009 to 2019. A total of 20,259 thyroid cancer patients aged from 15 to 50 years (8037 did not receive 131I treatment, and 12,222 received 131I treatment) and 20,259 controls were enrolled. A Cox proportional hazards model was applied to estimate the risk of infertility in thyroid cancer patients receiving or not receiving 131I treatment in terms of hazard ratios and 95% confidence intervals.

RESULTS

The incidence rates of infertility in thyroid cancer receiving 131I therapy, those not receiving 131I therapy, and controls were 5.55, 5.07, and 3.61 per 1000 person-years, respectively. Compared with thyroid cancer patients treated with a cumulative 131I dose of 4.44 GBq or less, the risk of infertility was not significantly increased in those treated with a cumulative 131I dose of more than 4.44 GBq (adjusted hazard ratio, 1.13; 95% confidence interval, 0.95-1.36).

CONCLUSIONS

The greatest increased risk of infertility in reproductive-age patients with thyroid cancer is associated with the fact that the patient has thyroid cancer regardless of 131I administration. 131I treatment or cumulative dose of 131I greater than 4.44 GBq did not further increase the risk of infertility.

Infertile Jordanian Women's Self-Perception About Societal Violence: An Interpretative Phenomenological Study

Background

Infertility affects one in six couples globally and is compounded by stigma and violence, particularly for women, in Jordan's culture. While existing research has illuminated societal pressures faced by infertile women, there is, yet no comprehensive understanding of the violence they encounter in their daily lives.

Objective

This Interpretative Phenomenological study seeks to unravel the experiences of infertile women regarding societal violence in Jordan. By focusing on different types of community violence - physical, psychological, and emotional - The study aims to provide nuanced insights into the challenges these women confront. It also endeavors to identify contributing factors, including societal attitudes, cultural beliefs, and individual encounters, while informing policy and practice to mitigate this issue.

Methods

Employing a qualitative approach, this study conducted semi-structured interviews with purposively sampled infertile women. Thematic analysis was utilized to uncover recurring patterns and themes, facilitating a comprehensive exploration of their experiences.

Results

Five main themes were identified: How the surrounding people view me as an infertile woman; I am suffocated by their questions; they interfere in the smallest details; I got burned and turned to ashes, and I have no right to complain; The problem of childbearing and the treatment plan is a matter for me and my husband only; and who supports me and what do I want from those around me?

Implications

This study's implications are significant for policy and practice. By foregrounding the prevalent violence faced by infertile women, it underscores the urgency of interventions. Raising awareness, providing education, and extending support can counteract societal stigma and violence. Creating a more compassionate societal fabric can ensure a safer, more inclusive environment for these women.

Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations

Background

Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern.

Objective

The study aimed to identify feature importance in a machine learning model using solely nutrition covariates.

Methods

A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

Results

7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis (p < 0.01 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.603, Sensitivity = 0.943, Specificity = 0.163. The top four highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Potassium Intake (Gain = 6.8%), Vitamin E Intake (Gain = 5.7%), Number of Foods and Beverages Reported (Gain = 5.7%), and Vitamin K Intake (Gain 5.6%).

Conclusion

Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.