Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review.

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BACKGROUND: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

[Analysis of the evolution of patients attended in Spanish emergency departments during the first wave of the pandemic].

BACKGROUND: To describe the number of visits (total and per COVID-19) attended by the Spanish hospital emergency departments (EDs) during the first wave of the pandemic (March-April 2020) compared to the same period in 2019, and to calculate the quantitative changes in healthcare activity and investigate the possible influence of hospital size and COVID-19 seroprevalence. METHOD: Cross-sectional study that analyzes the number of visits to Spanish public EDs, reported through a survey of ED chiefs during the study periods. Changes in healthcare activity were described in each autonomous community and com-pared according to hospital size and the provincial impact of the pandemic. RESULTS: A total of 187 (66?%) of the 283 Spanish EDs participated in the study. The total number of patients attended de-creased to 49.2?% (

Regulating During Crisis: A Qualitative Comparative Case Study of Nursing Regulatory Responses to the COVID-19 Pandemic.

Background: The COVID-19 pandemic placed intense pressure on nursing regulatory bodies to ensure an adequate healthcare workforce while maintaining public safety. Purpose: Our objectives were to analyze regulatory bodies' responses during the pandemic, examine how nursing regulators conceptualize the public interest during a public health crisis, and explore the influence of a public health crisis on the balancing of regulatory principles. We aimed to develop a clearer understanding of regulating during a crisis by identifying themes within regulatory responses. Methods: We conducted a qualitative comparative case study examining the pandemic responses of eight nursing regulators in three Canadian provinces and three U.S. states. Data were collected from semi-structured interviews with 19 representatives of nursing regulatory bodies and 206 publicly available documents and analyzed thematically. Results: Five themes were constructed from the data: (1) risk-based responses to reduce regulatory burden; (2) agility and flexibility in regulatory pandemic responses; (3) working with stakeholders for a systems-based approach; (4) valuing consistency in regulatory approaches across jurisdictions; and (5) the pandemic as a catalyst for innovation. Specifically, we identified that the meaning of "public interest" in the context of high workforce demand was a key consideration for regulators. Conclusion: Our results demonstrate the intensity of effort involved in nursing regulatory responses and the significant contribution of nursing regulation to the healthcare system's pandemic response. Our results also indicate a shift in thinking around broader public interest issues, beyond the conduct and competence of individual nurses, to include pressing societal issues. Regulators are beginning to grapple with these longer-term issues and policy tensions.

HPR80 Assessing Performance of TB Management Through NTEP By 2025: An Initiative From the Indian Government a Roadmap Ahead of Sustainable Goal in India 2030

Objectives: There are studies highlighting TB trends in association with several factors like demographics, drug resistance, etc, but there is dearth of literature on performance of NTEP and its association with TB trends. Hence, present research aims at assessing the TB trend in association with NTEP from its inception, budget allocation, and expenditure. Further, study will highlight one of the best performing states in NTEP implementation strategies. Method(s): Its a retrospective study, data was extracted and analysed from official websites of the Central TB Division, National strategic plan reports, PubMed, and other grey literature. Study excluded literature on paediatric patients. Result(s): Study findings indicate trend of TB based on incidence, prevalence, and mortality rate for a period of 8 years starting from 2012 to 2020. From the trend, it's clear that mortality, incidence and prevalence rates are decreasing but impact of covid makes variations for the same. Looking at budget allocation, spending pattern, between 2012 to 2018 there was surplus of funds, whereas between 2019 to 2021 there is deficit. Trend analysis has revealed that the NTEP in India is performing well despite the pandemic effect. Conclusion(s): The study reported trends which shows mortality and incidence rates of tuberculosis in India are decreasing. But prevalence rate trend is increased in 2016 and 2017 due to the comorbidity condition like HIV and inaccuracy of data that is found in TB national report. The requested fund and approved budgeted fund differ significantly with actual fund released to the states. The budget and expenditure trend have revealed that allotted budget was underutilized in the early-stages and later expenditures exceed the budget, or the budget is overutilized. Finally, as per NTEP implementation among all the states, Assam is found to be one of the states that excels in outperforming on TB eradication in India with more data transparency.Copyright © 2022

Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study.

BACKGROUND: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed. OBJECTIVE: The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period. METHODS: Linear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value. RESULTS: Participants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI -30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps. CONCLUSIONS: Participants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship. TRIAL REGISTRATION: ClinicalTrials.gov NCT04663945; https://tinyurl.com/2p969ced.

Utilizing Community Based Participatory Research Methods in Black/African American and Hispanic/Latinx Communities in the US: The CDC Minority HIV Research Initiative (MARI-Round 4).

The Centers for Disease Control and Prevention Minority HIV Research Initiative (MARI) funded 8 investigators in 2016 to develop HIV prevention and treatment interventions in highly affected communities. We describe MARI studies who used community-based participatory research methods to inform the development of interventions in Black/African American and Hispanic/Latinx communities focused on sexual minority men (SMM) or heterosexual populations. Each study implemented best practice strategies for engaging with communities, informing recruitment strategies, navigating through the impacts of COVID-19, and disseminating findings. Best practice strategies common to all MARI studies included establishing community advisory boards, engaging community members in all stages of HIV research, and integrating technology to sustain interventions during the COVID-19 pandemic. Implementing community-informed approaches is crucial to intervention uptake and long-term sustainability in communities of color. MARI investigators' research studies provide a framework for developing effective programs tailored to reducing HIV-related racial/ethnic disparities.

Practical guidance for telemedicine use in neuro-oncology.

While the COVID-19 pandemic has catalyzed the expansion of telemedicine into nearly every specialty of medicine, few articles have summarized current practices and recommendations for integrating virtual care in the practice of neuro-oncology. This article identifies current telemedicine practice, provides practical guidance for conducting telemedicine visits, and generates recommendations for integrating virtual care into neuro-oncology practice. Practical aspects of telemedicine are summarized including when to use and not use telemedicine, how to conduct a virtual visit, who to include in the virtual encounter, unique aspects of telehealth in neuro-oncology, and emerging innovations.

Patient-reported disruptions to cancer care during the COVID-19 pandemic: A national cross-sectional study.

BACKGROUND: The aim of this study is to evaluate the extent and associations with patient-reported disruptions to cancer treatment and cancer-related care during the COVID-19 pandemic utilizing nationally representative data. METHODS: This analysis uses data from the 2020 National Health Interview Survey (NHIS), an annual, cross-sectional survey of US adults. Adults (age >18) who reported requiring current cancer treatment or other cancer-related medical care in the second half of 2020 were included. Estimated proportions of patients with self-reported changes, delays, or cancelations to cancer treatment or other cancer care due to the COVID-19 pandemic were calculated using sampling weights and associations with sociodemographic and other health-related variables were analyzed. RESULTS: In total, 574 (sample-weighted estimate of 2,867,326) adults reported requiring cancer treatment and/or other cancer care since the start of the COVID-19 pandemic. An estimated 32.1% reported any change, delay, or cancelation. On sample-weighted univariable analysis, patients who were younger, female, had one or fewer comorbidities, and uninsured were significantly more likely to report disruptions. On sample-weighted, multivariable analysis, patients who were younger and female remained significant predictors. Nearly 90% of patients included in the study reported virtual appointment use. Patients reporting disruptions were also significantly more likely to report feelings of anxiety. CONCLUSIONS: An estimated 1/3 of patients experienced disruptions to cancer care due to the COVID-19 pandemic. Patients experiencing disruptions in care were more likely to be female or younger which may reflect risk stratification strategies in the early stages of the pandemic, and also had higher rates of anxiety. The longitudinal impact of these disruptions on outcomes merits further study.

Assessing the Relationship Between Emergency Food Assistance and Social Vulnerability During the COVID-19 Pandemic.

Food insecurity is a major public health challenge that is associated with negative health outcomes in wealthy countries. In US urban areas, food banks and pantries played an expanded role in providing emergency food assistance and addressing food insecurity during the COVID-19 pandemic. This study seeks to determine if socially vulnerable neighborhoods are more likely to receive emergency food assistance during this pandemic, after controlling for distance to emergency food distribution sites and spatial clustering. The study area is El Paso County, Texas-an urban area on the US-Mexico border. Dependent variables represent both coverage and intensity of emergency food transfers (EFTs) from local food banks and pantries during November 2020, at the census tract level. Independent variables are derived from the widely used Social Vulnerability Index (SVI) developed by the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry. Our statistical analyses are based on multivariable generalized estimating equations that account for spatial clustering and proximity to emergency food distribution sites. Results indicate that both coverage and intensity of EFTs are significantly greater in neighborhoods with higher social vulnerability and proximity to emergency food distribution sites, but lower in neighborhoods that are more vulnerable in terms of housing and transportation. Our findings highlight the significance of neighborhood-level social factors in influencing access to the emergency food network during a public health crisis and have important implications for government agencies and nonprofit organizations associated with public health and emergency preparedness in US urban areas.

The Impact of the COVID-19 Pandemic on Kidney Transplant Candidate Waitlist Status across Demographic and Geographic Groups: A National Analysis of UNOS STAR Data.

The primary goal of this retrospective study is to understand how the COVID-19 pandemic differentially impacted transplant status across race, sex, age, primary insurance, and geographic regions by examining which candidates: (i) remained on the waitlist, (ii) received transplants, or (iii) were removed from the waitlist due to severe sickness or death on a national level. Methods: The trend analysis aggregated by monthly transplant data from 1 December 2019 to 31 May 2021 (18 months) at the transplant center level. Ten variables about every transplant candidate were extracted from UNOS standard transplant analysis and research (STAR) data and analyzed. Characteristics of demographical groups were analyzed bivariately using t-test or Mann-Whitney U test for continuous variables and using Chi-sq/Fishers exact tests for categorical variables. Results: The trend analysis with the study period of 18 months included 31,336 transplants across 327 transplant centers. Patients experienced a longer waiting time when their registration centers in a county where high numbers of COVID-19 deaths were observed (SHR < 0.9999, p < 0.01). White candidates had a more significant transplant rate reduction than minority candidates (-32.19% vs. -20.15%) while minority candidates were found to have a higher waitlist removal rate than White candidates (9.23% vs. 9.45%). Compared to minority patients, White candidates' sub-distribution hazard ratio of the transplant waiting time was reduced by 55% during the pandemic period. Candidates in the Northwest United States had a more significant reduction in the transplant rate and a greater increase in the removal rate during the pandemic period. Conclusions: Based on this study, waitlist status and disposition varied significantly based on patient sociodemographic factors. During the pandemic period, minority patients, those with public insurance, older patients, and those in counties with high numbers of COVID-19 deaths experienced longer wait times. In contrast, older, White, male, Medicare, and high CPRA patients had a statistically significant higher risk of waitlist removal due to severe sickness or death. The results of this study should be considered carefully as we approach a reopening world post-COVID-19, and further studies should be conducted to elucidate the relationship between transplant candidate sociodemographic status and medical outcomes during this era.

COVID-19 and outdoor recreation in the post-anthropause

The lockdown measures instituted during the early months of the COVID-19 pandemic resulted in a moment of restricted human activity and mobility that researchers have called the 'anthropause'. Along with accounts of the widespread suspension or disruption of various industries, including sport, recreation, and tourism, media reported on the anthropause's positive impact on wildlife and environments, evidenced by accounts of animals returning to their previously displaced habitats and thriving in spaces typically marked by human activity. However, the period following these lockdown measures witnessed the re-opening of disrupted industries, and also a marked increase in outdoor human activity, particularly via engagement with forms of outdoor recreation at national and state parks and other protected areas. This analysis asserts that during this post-anthropause, the renewal and increase in outdoor recreation practices within protected areas re-demonstrated the ecological impacts of human activity within those spaces. Utilising media reports regarding outdoor recreation and US national and state parks during the pandemic, this essay explores the implications of leisure after lockdown, arguing that the post-anthropause represents an important conceptual tool for better understanding the complex relations between physical cultures, environments, and the anthropocentric dictates of contemporary 'burnout society'. [ FROM AUTHOR]

Predicting physical distancing over time during COVID-19: testing an integrated model.

Objective: We applied an integrated social cognition model to predict physical distancing behavior, a key COVID-19 preventive behavior, over a four-month period. Design: A three-wave longitudinal survey design. Methods: Australian and US residents (N = 601) completed self-report measures of social cognition constructs (attitude, subjective norm, moral norm, perceived behavioral control [PBC]), intention, habit, and physical distancing behavior on an initial occasion (T1) and on two further occasions one week (T2) and four months (T3) later. Results: A structural equation model revealed that subjective norm, moral norm, and PBC, were consistent predictors of physical distancing intention on all three occasions. Intention and habit at T1 and T2 predicted physical distancing behavior at T2 and T3, respectively. Intention at T2 mediated effects of subjective norm, moral norm, and PBC at T2 on physical distancing behavior at T3, and habit at T1 and T2 mediated effects of behavior at T1 and T2 on follow-up behavior at T2 and T3, respectively. Conclusion: Normative (subjective and moral norms) and capacity (PBC) constructs were consistent predictors of physical distancing intention, and intention and habit were consistent predictors of physical distancing behavior. Interventions promoting physical distancing should target change in normative and personal capacity beliefs, and habit.Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2021.1968397 .

Utilisation of deep learning for COVID-19 diagnosis.

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Variation in Hand Hygiene Improvement After Implementation of an Electronic Hand Hygiene System During the COVID-19 Pandemic

Background. Electronic hand hygiene (HH) monitoring systems have many potential advantages but there are limited data on wide-scale implementation of these systems. Methods. We deployed an electronic HH monitoring system in over 2,100 acute and critical care rooms across 9 hospitals in an academic health system. Badges with a Bluetooth beacon were issued to over 7,000 healthcare workers. Deployment began in early 2020 and was interrupted by the pandemic. The rollout of interventions to improve HH adherence was managed at the hospital level. Healthcare-associated infections (HAIs) were determined by the infection prevention team using standard CDC definitions. Hospital-level HH adherence rates were compared to a composite SIR including SIRs for CLABSI, CAUTI, hospital-onset MRSA bloodstream infections and hospital-onset Clostridiodes difficile infections. Results. Between January 2020 and April 2022, there were over 36 million hand hygiene opportunities with an average of 19 observations per staffed room per day. Overall HH adherence improved from 46% to 60%, with significant variation by hospital (4 improving by >25% and 3 by < 5%). Hospitals whose implementation was most delayed showed the least improvement. Preliminary analysis found no relationship between hand hygiene improvement and the SIR composite aggregated by calendar year. Conclusion. Despite the challenges of large-scale implementation of an electronic HH system during a pandemic, we demonstrated an overall improvement in HH adherence. The wide variation in improvement among hospitals was due to timing of implementation, variation in the dedicated hospital-specific project management resources and leadership engagement. In addition to technology, successful implementation of electronic HH systems requires dedicated resources and culture change. Pandemic-related staffing challenges, disruption of standard HAI prevention efforts and intensive device utilization confounded our ability to show a relationship between HH adherence and HAI rates.

Antibiotic-resistant gram-negative bacterial infections among persons with or without a prior positive test for SARS-CoV-2 in 10 U.S. sites, 2020

Background. The Centers for Disease Control and Prevention's Emerging Infections Program (EIP) conducts active laboratory- and population-based surveillance for carbapenem-resistant Enterobacterales (CRE), extended spectrum beta-lactamase-producing Enterobacterales (ESBL-E), and carbapenem-resistant Acinetobacter baumannii (CRAB) in 10 U.S. sites. To describe the impact of the COVID-19 pandemic on the epidemiology of these antibiotic-resistant gram-negative bacteria (AR-GNB), we assessed characteristics of AR-GNB patients with and without a prior SARS-CoV-2 positive (SC2+) viral test. Methods. In 2020 among EIP catchment-area residents, an incident CRAB or CRE case was defined as the first isolation of A. baumannii complex, Escherichia coli, Enterobacter cloacae complex, Klebsiella aerogenes, K. oxytoca, K. pneumonia, or K. variicola in a 30-day period resistant to >=1 carbapenem (excluding ertapenem for CRAB) from a normally sterile site or urine. An incident ESBL-E case was defined as the first isolation of E. coli, K. pneumonia, or K. oxytoca in a 30-day period resistant to any third-generation cephalosporin and non-resistant to all carbapenems from a normally sterile site or urine. Patient charts were reviewed. Results. Of 3904 AR-GNB cases with data available, 163 (4%) had a prior SC2+ test (85 ESBL-E, 70 CRE, and 8 CRAB). Median time from the most recent SC2+ test to AR-GNB culture date was 20 days (IQR 1-48 days). AR-GNB cases with a SC2+ test versus those without were more likely to be Black, non-Hispanic than another race/ ethnicity (31% vs 15%;P< 0.0001), aged >=65 years (62% vs 52%;P=0.0139), and to have prior healthcare exposures (63% vs 49%;P=0.0003) and indwelling devices (51% vs 28%;P< 0.0001). They were also more likely to have bacteremia (24% vs 11%;P< 0.0001), pneumonia (6% vs 1%;P< 0.0001) and be hospitalized around the time of their AR-GNB culture (67% vs 36%;P< 0.0001);median time from SC2 + test to hospital admission was 0.5 day (IQR 0-29.5 days). Conclusion. AR-GNB infections preceded by a SC2+ test were rare but more severe and associated with more healthcare risk factors. This underscores the need for continued infection prevention and control practices and monitoring of these infections during the COVID-19 pandemic.

Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).

OBJECTIVES: Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation. METHODS: The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC). RESULTS: Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001). CONCLUSION: In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models. KEY POINTS: • The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

Clinical Profile, Maternal and Perinatal Outcome in Sars-Cov2 Infection in Pregnancy

Introduction: Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Maternal physiological adaptations in pregnancy, and the physiological state of relative immune suppression, place pregnant women at increased risk of infection [1,2]. The present study is important due to the tremendous impact Covid 19 has on people at large, especially expectant mothers. In our study, we collected information on pregnant women with confirmed SARS-CoV-2 infection. Aim and Objective: 1) To estimate clinical features, maternal and perinatal outcome of Covid 19, during first, second and third wave of covid pandemic 2) To compare the Obstetric outcome in first and second wave with third wave. 3) To estimate vertical transmission to new born child in this institution as evidenced by test positivity. Method(s): Retrospective observational study was designed to examine the clinical characteristics and outcome of covid positive pregnancies admitted in our institution. Result(s): In our study of 266 pregnant women with covid, it was noticed that the mean age of the patients was found to be 27.55 years with a standard deviation of +/-4.99 years. 55.64% of cases belonged to category B1, 33.08% in B2 and 11.28% in C. 2nd wave had more patients in category C. Gestational diabetes complicated 28.95% and hypertension in 17.29% of study population. Inflammatory markers were more elevated in 2nd and 3rd wave. There was a total maternal death of 11 patients. Out of this, 10 was (91%) due to covid pneumonia and ARDS. Breast feeding was given for 88.7% of the babies and for 88% of the babies rooming in was practiced. Only 2.6% of the babies turned positive within a week. Conclusion(s): Our study shows that expectant mothers were more severely affected in the second wave. Maternal mortality was associated with increased maternal age (> 35 years), raised CRP levels (> 75mg/L) and higher D dimer levels (> 3000 ng/ml) and is found to be statistically significant. There is no evidence to show any vertical transmission of the disease as only 2.1% of the neonates (7nos) were affected within a week. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.