Impact of COVID-19 Pandemic on the Clinical Activities in Obstetrics and Gynecology: A National Survey in China

Associations of the implementation of the three-child policy with changes in contraceptive methods among women who underwent induced abortion: a cross-sectional study of Wuhan, a megacity in central China

BACKGROUND

Induced abortion, as a legal medical practice in China, has an enormous negative impact on women's sexual and reproductive health. After the implementation of the three-child policy, guiding women to choose appropriate contraceptive measures to protect their fertility is the focus of China's family planning. But little is known about the impact of the family planning policy on women's contraceptive behaviour.

METHODS

We collected 7022 cases who underwent abortion between July 2020 and June 2022 in Wuhan. Based on the time of implementation of the policy, the study population was divided into two groups, namely, the pre-policy group (before July 2021, Group 1) and the post-policy group (after July 2021, Group 2). Logistic regression was used to analyse the effect of the policy on women's choice of different contraceptive methods for abortion.

RESULTS

There were a total of 3487 cases in Group 1, accounting for 49.65%, and 3535 cases in Group 2, accounting for 50.34%. The proportion of women choosing efficient contraceptive methods after the Three-Child Policy decreased compared with before. A correlation was observed between the implementation of the Three-Child Policy and whether to choose efficient contraceptive methods after abortion (p < 0.001).

CONCLUSION

The study has found that after the Three-Child Policy, women opting for induced abortion tend to prefer less effective contraceptive methods, particularly condoms. There is a marked shift towards more effective contraception methods both before and after the implementation of the Three-Child Policy.

How to promote telemedicine in underdeveloped cities in central China? Qualitative interviews with medical personnel in tertiary A-level hospital of Xinyang City

Background

Telemedicine has emerged as a novel healthcare service model that plays a vital role in addressing the unequal distribution of medical resources. Telemedicine has recently gained significant traction in economically prosperous cities such as Beijing, Shanghai, and Guangzhou in China. However, Xinyang City in Henan Province is an economically less developed city, and telemedicine is still in its early stages. By exploring the views of medical staff and administrators at Xinyang City's tertiary A-level hospital on telemedicine, this study aims to determine the development status of telemedicine services in Xinyang City. The objective was to identify the challenges in the construction process and formulate corresponding strategies for advancing telemedicine in Xinyang City.

Methods

Face-to-face personal interviews were conducted with 16 medical staff and administrators from a tertiary A-level hospital in Xinyang City, Henan Province, and the data were collected and analyzed using grounded theory. In the process of data analysis, NVivo12 software was used to encode and organize the data line by line.

Results

The development of telemedicine in Xinyang City has the potential to benefit residents, hospitals, and healthcare personnel despite several challenges, including the absence of laws and regulations, inadequate policy support, limited hospital cooperation, and low resident awareness, which must be addressed to unlock the full potential of telemedicine.

Conclusion

The telemedicine system in Xinyang City is currently in a coordinated developmental phase, and several areas require further improvement. The development of standardized telemedicine in Xinyang City requires government support, better training for general practitioners, public awareness campaigns, and improved technology while ensuring reasonable work schedules and motivating medical personnel.

The effect of fear of COVID-19 on health-seeking behaviors and Pap smear test rates in women

This study was carried out to determine the effect of fear of COVID-19 on health-seeking behaviors and Pap smear test rates in women. This study was conducted online between June and September 2021. Women aged 21-65 years were invited to participate in the study via social media. A total of 396 women comprised the sample for this study. Data were collected the Fear of COVID-19 Scale, and the Health-seeking Behavior Scale. Data were evaluated using descriptive statistics, parametric tests and regression analysis. As a result of the study, 11.1 percent of women were found to have had a Pap smear test during the pandemic period. Fear of COVID-19 was determined to have a positive and significant effect on online health-seeking behavior (t = 7.915, p < .001), professional health-seeking behavior (t = 4.503, p < .001), traditional health-seeking behavior (t = 6.037, p < .001), and general health-seeking behavior(t = 8.390, p < .001) while it did not have any effect on having a Pap smear test (p > .05). This study showed that although the fear of COVID-19 positively affected health-seeking behaviors in women, the rates of gynecological examination and Pap smear test rates were low during the pandemic.

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.