ABSTRACT
Background: The COVID-19 infection may adversely affect both the mother and baby. Evaluation and identification of aggravating factors can help prevent adverse outcomes.Objectives: The present study aimed to examine pregnant women with COVID-19 infection and evaluate the disease outcomes inMethods: The present case series study was performed on 17 pregnant women hospitalized for COVID-19 in Sari. A convenience sampling method was used. First, the researcher took the demographic information and medical history and obtained informed consent from all participants. Then, the selected subjects were examined for inclusion and exclusion criteria, and a throat swab sample was taken from eligible ones for PCR. The PCR was performed for amniotic fluid and neonatal throat samples at pregnancy termination. Six weeks after delivery, the status of rehospitalization of the baby, breastfeeding status, rehospitalization of the mother due to COVID-19, and the state of depression of the mother were evaluated by a 21-item questionnaire over the phone. The collected data were analyzed in SPSS version 23 using the Chi-square test.Results: Out of 19 participants, 17 (68%) had positive results for COVID-19 laboratory tests. The prevalence of preterm labor, admission to the neonatal intensive care unit, and vertical transmission were significantly high in pregnant women with COVID-19 and positive PCR results for amniotic fluid (P < 0.050). The frequency of admission to the ICU was significantly higher in pregnant women with diabetes infected with COVID-19 (P = 0.025). There was no rehospitalization of the mother and newborn due to COVID-19, but one case of postpartum depression (9.5%) and two cases of formula feeding (11.8%) were reported.Conclusions: Due to the high risk of maternal and neonatal outcomes of COVID-19 during pregnancy and the high probability of vertical transmission, it is recommended to take special precautions to prevent the disease during this period.
ABSTRACT
The outbreak of the 2019 novel coronavirus disease (COVID-19) has infected 4 million people worldwide and has caused more than 300, 000 deaths worldwide. With infection and death rates on rise, COVID-19 poses a serious threat to social functioning, human health, economies, and geopolitics. Geographic information systems and big geospatial technologies have come to the forefront in this fight against COVID-19 by playing an important role by integrating multisourced data, enhanced and rapid analytics of mapping services, location analytics, and spatial tracking of confirmed, forecasting transmission trajectories, spatial clustering of risk on epidemiologic levels, public awareness on the elimination of panic spread and decision-making support for the government and research institutions for effective prevention and control of COVID-19 cases. Big geospatial data has turned itself as the major support system for governments in dealing with this global healthcare crisis because of its advanced and innovative technological capabilities from preparation of data to modeling the results with quick and large accessibility to every spatial scale. This robust data-driven system using the accurate and prediction geoanalysis is being widely used by governments and public health institutions interfaced with both health and nonhealth digital data repositories for mining the individual and regional datasets for breaking the transmission chain. Profiling of confirmed cases on the basis of location and temporality and then visualizing them effectively coupled with behavioral and critical geographic variables such as mobility patterns, demographic data, and population density enhance the predictive analytics of big geospatial data. With the intersection of artificial intelligence, geospatial data enables real-time visualization and syndromic surveillance of epidemic data based on spatiotemporal dynamics and the data are then accurately geopositioned. This chapter aims to reflect on the relevance of big geospatial data and health geoinformatics in containing and preventing the further spread of COVID-19 and how countries and research organizations around the world have used it as accurate, fast, and comprehensive dataset in their containing strategy and management of this public health crisis. China and Taiwan are used as case studies as in how these countries have applied the computational architecture of big geospatial data and location analytics surveillance techniques for prediction and monitoring of COVID-19-positive cases. © 2022 Elsevier Inc.
ABSTRACT
Objective: Self-medication is a worldwide practice in which individuals, families, and/or communities choose pharmaceuticals to address health conditions without consulting a doctor. It impacts the health of people both negatively as well as positively. This study aims to determine the prevalence of self-medication for COVID-19 like symptoms during the pandemic. Methods: This is an online questionnaire-based survey on the perceptions and use of certain drugs for COVID-like symptoms during the COVID-19 pandemic. 168 people responded to the questionnaire. Results: Out of 168 respondents, 53.0% were males. 71.4% were below 30 years of age and, 25.6% were 31-60 years. The majority (72.6%) were unmarried. 50.0% had studied up to university level. 49.4% were unemployed. 39.9% were healthcare workers. 59.9% had suffered from respiratory symptoms during the COVID-19 pandemic. All those who developed symptoms had self-medicated. The most commonly used drugs were Paracetamol (85.0%), Azithromycin (58.0%), Expectorants (30.0%), Ivermectin (18.0%), Doxycycline (16.0%), Corticosteroids (7.0%), and Hydroxychloroquine (4.0%). The major sources of information about the disease and drugs were pharmacists (46.6%) and the internet (28.0%). Conclusion: There were significant percentages of self-medication during the COVID-19 pandemic, including the drugs without sufficient scientific evidence.