COVID-19 literature surveillance-A framework to manage the literature and support evidence-based decision-making on a rapidly evolving public health topic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has led to a rapid surge of literature on severe acute respiratory syndrome coronavirus 2 and the wider impacts of the pandemic. Research on COVID-19 has been produced at an unprecedented rate, and the ability to stay on top of the most relevant evidence is top priority for clinicians, researchers, public health professionals and policymakers. This article presents a knowledge synthesis methodology developed and used by the Public Health Agency of Canada for managing and maintaining a literature surveillance system to identify, characterize, categorize and disseminate COVID-19 evidence daily. Methods: The Daily Scan of COVID-19 Literature project comprised a systematic process involving four main steps: literature search; screening for relevance; classification and summarization of studies; and disseminating a daily report. Results: As of the end of March 2022 there were approximately 300,000 COVID-19 and pandemic-related citations in the COVID-19 database, of which 50%-60% were primary research. Each day, a report of all new COVID-19 citations, literature highlights and a link to the updated database was generated and sent to a mailing list of over 200 recipients including federal, provincial and local public health agencies and academic institutions. Conclusion: This central repository of COVID-19 literature was maintained in real time to aid in accelerated evidence synthesis activities and support evidence-based decision-making during the pandemic response in Canada. This systematic process can be applied to future rapidly evolving public health topics that require the continuous evaluation and dissemination of evidence.

The role of models as a decision-making support tool rather than a guiding light in managing the COVID-19 pandemic.

Reference scenarios based on mathematical models are used by public health experts to study infectious diseases. To gain insight into modeling assumptions, we analyzed the three major models that served as the basis for policy making in Israel during the COVID-19 pandemic and compared them to independently collected data. The number of confirmed patients, the number of patients in critical condition and the number of COVID-19 deaths predicted by the models were compared to actual data collected and published in the Israeli Ministry of Health's dashboard. Our analysis showed that the models succeeded in predicting the number of COVID-19 cases but failed to deliver an appropriate prediction of the number of critically ill and deceased persons. Inherent uncertainty and a multiplicity of assumptions that were not based on reliable information have led to significant variability among models, and between the models and real-world data. Although models improve policy leaders' ability to act rationally despite great uncertainty, there is an inherent difficulty in relying on mathematical models as reliable tools for predicting and formulating a strategy for dealing with the spread of an unknown disease.

Innovation and Evidence-Based Decision-Making

Merger and acquisition activities in health care are increasing in both the number and cumulative value of transactions in recent years, creating new and dynamic pressures on health care systems and current operating environments. These industry shifts, coupled with crises such as the COVID-19 global pandemic, create opportunities for innovation to increase capacity, improve productivity, achieve economies of scale, and positively impact health care quality, safety, access, and cost. However, neither consolidation nor innovation in and of themselves will yield sustainable clinical best practices nor achieve the desired quality, financial, efficiency, retention, or engagement outcomes. This article describes the approach used by one system-level Doctor of Nursing Practice prepared nurse executive to leverage evidence-based decision-making to guide, lead, and support the innovation needed to address first-year new graduate nurse turnover in a multistate not-for-profit health care system.

Automatic identification and explanation of root causes on COVID-19 index anomalies.

This paper reports a method for automatically identifying, analyzing and explaining anomalies in different indexes of COVID-19 crisis using Artificial Intelligence (AI) based techniques. With systematic application of News sensor, language detection & translation, Keyword-based extraction of COVID-19 indexes, Convolutional Neural Network (CNN) based anomaly detection, and Natural Language Processing (NLP) based explanation methods, this paper demonstrates a methodological solution for strategic decision makers to make evidence-based policy decisions on COVID-19 (in multiple dimensions like Travel, Vaccine, Medical etc.). Firstly, COVID-19 related News is fetched from multiple sources in multiple languages. Then, AI-based language detection and translation process automatically translates these News and posts in real-time. Next, COVID-19 related News and posts are segregated in multiple groups using pre-defined keywords for creation of multiple indexes. Lastly, CNN based anomaly detection identifies all the anomalies on multiple COVID-19 indexes with NLP-based explanations. A standalone decision support system was developed that implemented the presented method. This decision support system allows a strategic decision-maker to comprehend "when, where, and why there are fluctuations in COVID-19 related sentiments on a particular dimension". Method was validated with Tweets from 15/072021 to 24/05/2022 resulting in automated generation of 5 COVID-19 indexes and 69 anomalies with explanations. In summary, this method of anomaly detection on COVID-19 indexes presents:•An explicit, transferable and reproducible procedure for detecting anomalies on multiple indexes of COVID-19 in multiple languages•It helps a strategic decision maker to comprehend the root-causes of anomalies in COVID-19 related travel, vaccine, medical indexes•The solution developed using the presented method allows evidence-based strategic decision-making COVID-19 crisis using AI, Deep Learning and NLP.

COVID-19 vaccine policy development in a sample of 44 countries - Key findings from a December 2021 survey of National Immunization Technical Advisory Groups (NITAGs).

National Immunization Technical Advisory Committees (NITAGs) are tasked with the responsibility of guiding ministries of health and national immunization programmes in their policy development processes. Many NITAGs rely on evidence reviewed by the World Health Organization's (WHO) Strategic Group of Experts(SAGE) on immunization and aim to adapt WHO's recommendations to their respective contexts. This relationship took on exceptional importance since the onset of the COVID-19 pandemic, during which NITAGs have expressed a notable struggle to craft appropriate policies on population prioritization and vaccine utilization in the face of supply constraints and complex programmatic and delivery logistics. This online survey was conducted to assess the usefulness of the SAGE guidance documents for COVID-19 vaccine policies and to examine the persisting needs and challenges facing NITAGs. Results confirmed that SAGE recommendations concerning COVID-19 vaccines are easy to access, understand, and adapt. They have been found to be comprehensive and timely under the data and time constrained circumstances confronting SAGE. The Global NITAG Network (GNN) appears to be the most popular vehicle for addressing questions among high income countries, in contrast to lower income countries who favour WHO Country or Regional Offices. NITAGs place much value on interaction with other NITAGs, which requires facilitation and could benefit from increased opportunities, especially within regions. It is further noted that some NITAGs have had to tackle issues during the pandemic not typically considered by SAGE, such as supply chain logistics and vaccine demand. Learning from the COVID-19 experience offers opportunities to strengthen NITAGs and the pandemic recovery effort through the development of more concrete procedures and consideration of more varied types of data, including implementation effectiveness and uptake data. There is also an opportunity for an increasing involvement of Country Office WHO personnel to support NITAGs, while ensuring information and evidence needs of countries are adequately reflected in SAGE deliberations.

Systematic and meta-based evaluation on job satisfaction of village doctors: An urgent need for solution issue.

Background: Village doctors are the health "gatekeepers" of rural residents in most developing countries. They undertake a series of strenuous but pivotal missions, including prevention, diagnosis, and treatment of complicated diseases, sanitation services and management, and preventive healthcare and education tasks. Hence, it is of great importance to evaluate the village doctors' job satisfaction status, which is one of the most important indicators that can reflect the current working state, to provide guidelines for the healthcare policies. Methods: Literature search was conducted in 7 authoritative databases, including PubMed, EMBASE, Web of Science, and China National Knowledge Infrastructure (CNKI). Experts in the field of social medicine were consulted to achieve supplement and obtain relevant literature. China was selected as a representative of the village doctor system for the in-depth analysis. Building on the previous literature, we modified and proposed a novel strategy that can transform and integrate the outcome indicators to conduct a meta-based and quantitative assessment on job satisfaction. Results: A total of 37 publications and 23,595 village doctors were included in this research. The meta-analysis showed that the overall job satisfaction score of village doctors was 3.1858 (total score: 5.00), 95% CI: 2.9675-3.404, which represented the level of "neither satisfied nor dissatisfied." However, in the subsequent adjustment of publication bias, this score reduced to 2.7579, 95% CI: 2.5254-2.9904, which indicated a direct "dissatisfied" level. To discover the underlying causes, a holistic analysis of each dimension and influencing factors of job satisfaction was conducted, and the results demonstrated that "Financial Rewards" (2.49) was the most important factor causing dissatisfaction among village doctors, followed by "Job Security (2.52)" and "Work Stress (3.05)." Several important themes were also identified and assessed to explore the factors related to this topic. Conclusion: This study indicated that there is an urgent need to improve the working status of health workers in rural and remote areas, especially in the middle- and low-income countries. Health policy makers should not only improve the current remuneration and subsidies of village doctors but also guide the professional development and give them more job security to enhance the work stability of this group. More specifically, in the context of the COVID-19 pandemic, further surveys on job satisfaction of village doctors should be carried out to take targeted measures. Systematic review registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42021289139].

A Conceptual Approach to the Stakeholder Mapping of Energy Lab in Poor Urban Settings

Various efforts are presently being undertaken to set up and maintain open, inclusive, participatory, and transparent processes, whilst at the same time, strengthening stakeholder partnerships in implementing SDGs remains a challenge. This paper enriched the discussion of multi-stakeholder approaches through a dynamic multi-level system view of stakeholder mapping, along with important theoretical frameworks and key empirical results to tackle the lack of security of energy services in poor urban settings. The study attempted to develop comprehensive cases for Africa-based experiences of the pilot project launched through a set-up of an energy living lab in the Groenheuwel community, as well as achieve an improved understanding of social-technical benefits of gendered energy security and innovative solutions at the household level. The contents are two-fold. The first part assesses the theoretical models available for stakeholders and outcome mapping. The second part focuses on the preliminary identification of stakeholders and their primary interests at all levels. The results of this study found that the energy living lab in poor urban settings recognised the importance of stakeholder mapping and the development of new solutions. Findings indicated that all stakeholders should support the government in the development of policies and strategies. Findings also suggested that key players should proactively agree and negotiate with the local government on energy outcome measures. It was also found that multi-stakeholder involvement improved transparency and accountability for decision making.

Rapid analysis of local data to inform off-label tocilizumab use early in the COVID-19 pandemic.

The interleukin-6 receptor antagonist tocilizumab became widely used early in the coronavirus disease 2019 (COVID-19) pandemic based on small observational studies that suggested clinical benefit in COVID-19 patients with a hyperinflammatory state. To inform our local treatment algorithms in the absence of randomized clinical trial results, we performed a rapid analysis of the first 11 hospitalized COVID-19 patients treated with tocilizumab at our academic medical center. We report their early clinical outcomes and describe the process by which we assembled a team of diverse trainees and stakeholders to extract, analyze, and disseminate data during a time of clinical uncertainty.

Evidence-informed vaccination decision-making in countries: Progress, challenges and opportunities.

Countries face an increasingly complex vaccination landscape. As well as ever-changing infectious disease epidemiology, the number and diversity of vaccine-preventable diseases, vaccine products, and vaccine technologies continue to increase. To ensure that vaccination decision-making is transparent, country-owned and informed by sound scientific evidence, many countries have established national immunization technical advisory groups (NITAGs) to provide independent expert advice. The past decade has seen substantial growth in NITAG numbers and functionality, and there is now a need to consolidate this progress, by further capacity building, to ensure that NITAGs are responsive to the changing face of immunization over the next decade.

Using a low-cost, real-time electronic immunization registry in Pakistan to demonstrate utility of data for immunization programs and evidence-based decision making to achieve SDG-3: Insights from analysis of Big Data on vaccines.

BACKGROUND: Despite the proliferation of digital interventions such as Electronic Immunization Registries (EIR), currently, there is little evidence regarding the use of EIR data to improve immunization outcomes in resource-constrained settings. To achieve the Sustainable Development Goal (SDG) of ensuring healthy lives and well-being for all ages, particularly for newborns and children under the age of 5 (goal 3b), it is essential to generate and use quality data for evidence-based decision making to overcome barriers inherent in immunization systems. In Pakistan, only 66 % of children receive all basic vaccinations, and in Sindh province, the number is even lower at 49 %. In 2012, IRD developed and piloted Zindagi Mehfooz (Safe Life; ZM) ElR, an Android-based platform that records and analyses individual-level child data in real-time. In 2017 in collaboration with Expanded Programme for Immunization (EPI) Sindh, ZM was scaled-up across the entire Sindh province and is currently being used by 2521 government vaccinators in 1539 basic health facilities, serving >48 million population. OBJECTIVE: The study aims to demonstrate how big immunization data from the ZM-EIR is being leveraged in Sindh, Pakistan for actionable decision making via three use cases (a) improving performance management of vaccinators to increase geographical coverage, (b) quantifying the impact of provincial accelerated outreach activities, and (c) examining the impact of the COVID-19 pandemic on routine immunization coverage to help devise a tailored approach for future efforts. METHODS: From October 2017 to April 2020, more than 2.9 million children and 0.9 million women have been enrolled, and more than 22 million immunization events have been recorded in the ZM-EIR. We extracted de-identified data from ZM-EIR for January 1, 2019 - April 20, 2020, period. Given the needs of each use case, monthly and daily indicators on vaccinator performance (attendance and compliance), daily immunization visits, and the number of antigens administered were calculated. Geo-coordinate data of antigen administration was extracted and displayed on geographic maps using QGIS. All generated reports were shared at fixed frequency with various stakeholders, such as partners at EPI-Sindh, for utilization in decision making and informing policy. RESULT: Our use-cases demonstrate the use of EIR data for data-driven decision making. From January - December 2019, the monthly monitoring of program indicators helped increase the vaccinator attendance from 44% to 88%, while an 85 % increase in geographical coverage was observed in a polio-endemic super high-risk union council (SHRUC) in Karachi. The analysis of daily average antigens administered during accelerated outreach efforts (AOE) as compared to routine activities showed an increase in average daily Pentavalent-3, Measles-1, and Measles-2 vaccines administered by 103%, 154%, and 180% respectively. These findings helped decide to continue the accelerated effort in high-risk areas (compared to the entire province) rather than discontinuing the activity due to high costs. During COVID-19 lockdown, the daily average number of child immunizations reduced from 16,649 to 4335 per day, a decline of 74% compared to 6 months preceding COVID-19 lockdown. ZM-EIR data is currently helping to shape the planning and implementation of critical strategies to mitigate the impact of the COVID-19 pandemic. CONCLUSION: The big data for vaccines generated through EIRs is a powerful tool to monitor immunization work-force and ensure chronically missed communities are identified and covered through targeted strategies. Geospatial data availability and analysis is changing the way EPI review meetings occur with stakeholders, taking data-driven decisions for better planning and resource allocation. In the fight against COVID-19 pandemic, as governments gradually begin to shift from containing the outbreak to strategizing a plan for sustaining the essential health services, the countries that will emerge most successful are likely the ones who can best use technology and real-time data for targeted efforts.

Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis.

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated. Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis. Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5-15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated. Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

Community Use Of Face Masks And COVID-19: Evidence From A Natural Experiment Of State Mandates In The US.

State policies mandating public or community use of face masks or covers in mitigating the spread of coronavirus disease 2019 (COVID-19) are hotly contested. This study provides evidence from a natural experiment on the effects of state government mandates for face mask use in public issued by fifteen states plus Washington, D.C., between April 8 and May 15, 2020. The research design is an event study examining changes in the daily county-level COVID-19 growth rates between March 31 and May 22, 2020. Mandating face mask use in public is associated with a decline in the daily COVID-19 growth rate by 0.9, 1.1, 1.4, 1.7, and 2.0 percentage points in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively. Estimates suggest that as a result of the implementation of these mandates, more than 200,000 COVID-19 cases were averted by May 22, 2020. The findings suggest that requiring face mask use in public could help in mitigating the spread of COVID-19.