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Background: In Zambia, the true extent of SARS-CoV-2 infections is unknown because initial surveillance focused on patients with symptoms or severe disease. Antenatal sentinel surveillance had not been used to assess infection trends. The ANC COVID-19 surveillance study sought to determine SARS-CoV-2 seroprevalence and COVID-19 vaccine uptake among pregnant women. We provide insight into the study implementation, challenges encountered, best practices, and lessons learned.
Methods: A repeated cross-sectional seroprevalence survey was implemented at 39 health facilities in four districts from September 2021 to September 2022. Pregnant women aged 15-49 years were enrolled at their first antenatal care visits. An electronic questionnaire gathered demographics and other COVID-19 related information from consenting participants. A dried blood sample was collected to detect IgG antibodies using a multiplex bead assay. Seropositive results were categorized as infection, infection and vaccination or infection based on anti-RBD and anti-nucleocapsid test results. Problems and their root causes were identified as they occurred. Practical problem-solving strategies were devised, implemented, and monitored to ensure that goals were accomplished.
Results: In the primary analysis, 7% of the 9,221 samples collected from participants were not tested because they were missing. COVID-19 vaccine uptake of 9,111 pregnant women was assessed. Approximately 64% of participants were cumulatively seropositive for SARS-CoV-2 antibodies. Seroprevalence increased from 27.8% in September 2021 to 56.6% in July 2022. We observed an increase in vaccine coverage (0.5-27%) over time. Women aged 40-49 years old, without education and with prior COVID-19 infection were associated with higher vaccine uptake. The Delta variant of COVID-19 and the reallocation of health facilities between two partners delayed surveillance activities and increased the cost of implementation (e.g., the purchase of additional calibration and validation kits and DBS cards). Protocol deviations were attributed to the lack of experience in conducting research but, the district RAs repeatedly trained health facility staff to enhance their research knowledge.
Conclusions: Incorporating SARS-CoV-2 surveillance into routine antenatal care is feasible and potentially sustainable when existing health system infrastructure, human resources, and surveillance systems are leveraged. Yet, careful planning is needed to anticipate implementation challenges and ensure high-quality data collection.
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http://dx.doi.org/10.1186/s12889-025-21918-x | DOI Listing |
Acta Diabetol
September 2025
Department of Endocrinology & Metabolism, Medical College & Hospital, Kolkata, 88, College St. College Square, Kolkata, West Bengal, 700073, India.
Background And Aims: Gestational diabetes mellitus (GDM) is defined as glucose intolerance first identified during pregnancy that does not meet the criteria for overt diabetes. Its pathophysiology shares key features with type 2 diabetes mellitus (T2D), including insulin resistance and inflammation. Emerging evidence suggests that long non-coding RNAs (lncRNAs) are implicated in T2D.
View Article and Find Full Text PDFInfection
September 2025
Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre, Hvidovre, Denmark.
Purpose: To investigate bacteriology, antibiotic treatment and adverse birth outcomes (ABOs) in pregnancies with and without bacteriuria and urinary tract infections (UTIs) based on urine cultures and clinical diagnoses.
Methods: Registry-based cohort study.
Population: Pregnancies with at least one urine culture analysed at one of two hospitals in the Capital Region, Denmark, between 2015 and 2021.
Clin Nurs Res
September 2025
Xuzhou Medical University, Jiangsu Province, China.
This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation.
View Article and Find Full Text PDFFront Glob Womens Health
August 2025
Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana.
Background: Vulvovaginal Candidiasis (VVC) is a condition commonly caused by . It is the second most common infection of the female genitalia affecting many women worldwide. Studies have identified unhealthy genital care practices to be associated with the infection among women including expectant mothers.
View Article and Find Full Text PDFPEC Innov
June 2025
Department of Obstetrics, & Reproductive Science, University of Pittsburgh School of Medicine, Gynecology Pittsburgh, PA, USA.
Objectives: To explore the perceptions of pregnant patients who use substances regarding positive or negative clinician communication during obstetrical care.
Methods: We analyzed qualitative data from 85 semi-structured interviews with pregnant patients who reported or tested positive for substance use, which explored their interaction with obstetric providers during their first prenatal visit. This analysis focuses on patients' perceptions of negative versus positive clinician communication behaviors.