scholarly journals Monitoring populations at increased risk for SARS-CoV-2 infection in the community

2021 ◽  
Author(s):  
Emma Pritchard ◽  
Joel Jones ◽  
Karina Vihta ◽  
Nicole Stoesser ◽  
Philippa C Matthews ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Multiple Testing ◽  
Reverse Causality ◽  
Screening Process ◽  
Department Of Health ◽  
Increased Risk ◽  
Behavioural Surveillance ◽  
Geographical Regions ◽  
Health And Social Care

Background: The COVID-19 pandemic is rapidly evolving, with emerging variants and fluctuating control policies. Real-time population screening and identification of groups in whom positivity is highest could help monitor spread and inform public health messaging and strategy. Methods: To develop a real-time screening process, we included results from nose and throat swabs and questionnaires taken 19 July 2020-17 July 2021 in the UK's national COVID-19 Infection Survey. Fortnightly, associations between SARS-CoV-2 positivity and 60 demographic and behavioural characteristics were estimated using logistic regression models adjusted for potential confounders, considering multiple testing, collinearity, and reverse causality. Findings: Of 4,091,537 RT-PCR results from 482,677 individuals, 29,903 (0.73%) were positive. As positivity rose September-November 2020, rates were independently higher in younger ages, and those living in Northern England, major urban conurbations, more deprived areas, and larger households. Rates were also higher in those returning from abroad, and working in healthcare or outside of home. When positivity peaked December 2020-January 2021 (Alpha), high positivity shifted to southern geographical regions. With national vaccine roll-out from December 2020, positivity reduced in vaccinated individuals. Associations attenuated as rates decreased between February-May 2021. Rising positivity rates in June-July 2021 (Delta) were independently higher in younger, male, and unvaccinated groups. Few factors were consistently associated with positivity. 25/45 (56%) confirmed associations would have been detected later using 28-day rather than 14-day periods. Interpretation: Population-level demographic and behavioural surveillance can be a valuable tool in identifying the varying characteristics driving current SARS-CoV-2 positivity, allowing monitoring to inform public health policy. Funding: Department of Health and Social Care (UK), Welsh Government, Department of Health (on behalf of the Northern Ireland Government), Scottish Government, National Institutes of Health Research.

scholarly journals How Missing Discharge Diagnosis Data in Syndromic Surveillance Leads to Coverage Gaps

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kayley Dotson ◽  
Mandy Billman
Keyword(s):  
Public Health ◽  
Real Time ◽  
Random Sample ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Discharge Diagnosis ◽  
Disease Clusters ◽  
Department Of Health ◽  
Coverage Gap ◽  
Administrative Code

ObjectiveTo identify surveillance coverage gaps in emergency department (ED) and urgent care facility data due to missing discharge diagnoses.IntroductionIndiana utilizes the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) to collect and analyze data from participating hospital emergency departments. This real-time collection of health related data is used to identify disease clusters and unusual disease occurrences. By Administrative Code, the Indiana State Department of Health (ISDH) requires electronic submission of chief complaints from patient visits to EDs. Submission of discharge diagnosis is not required by Indiana Administrative Code, leaving coverage gaps. Our goal was to identify which areas in the state may see under reporting or incomplete surveillance due to the lack of the discharge diagnosis field.MethodsEmergency department data were collected from Indiana hospitals and urgent care clinics via ESSENCE. Discharge diagnosis was analyzed by submitting facility to determine percent completeness of visits. A descriptive analysis was conducted to identify the distribution of facilities that provide discharge diagnosis. A random sample of 20 days of data were extracted from visits that occurred between January 1, 2017 and September 6, 2017.ResultsA random sample of 179,039 (8%) ED entries from a total of 2,220,021 were analyzed from 121 reporting facilities. Of the sample entries, 102,483 (57.24%) were missing the discharge diagnosis field. Over 40 (36%) facilities were missing more than 90% of discharge diagnosis data. Facilities are more likely to be missing >90% or <19% of discharge diagnoses, rather than between those points.Comparing the percent of syndromic surveillance entries missing discharge diagnosis across facilities reveals large variability. For example, some facilities provide no discharge diagnoses while other facilities provide 100%. The number of facilities missing 100% of discharge diagnoses (n = 19) is 6.3 times that of the facilities that are missing 0% (n = 3).The largest coverage gap was identified in Public Health Preparedness District (PHPD)1 three (93.16%), with districts five (64.97%), seven (61.94%), and four (61.34%) making up the lowest reporting districts. See Table 2 and Figure 12 for percent missing by district and geographic distribution. PHPD three and five contain a large proportion (38%) of the sample population ED visits which results in a coverage gap in the most populated areas of the state.ConclusionsQuerying ESSENCE via chief complaint data is useful for real-time surveillance, but is more informative when discharge diagnoses are available. Indiana does not require facilities to report discharge diagnosis, but regulatory changes are being proposed that would require submission of discharge diagnosis data to ISDH. The addition of discharge diagnose is aimed to improve the completeness of disease clusters and unusual disease occurrence surveillance data.References1. Preparedness Districts [Internet]. Indianapolis (IN): Indiana State Department of Health, Public Health Preparedness; 2017 [Cited 2017 Sept 20]. Available from: https://www.in.gov/isdh/17944.htm. 

An influenza vaccination season like no other: present and future aspects

2021 ◽  
Vol 32 (Sup3a) ◽  
pp. S10-S14
Author(s):  
Pauline MacDonald
Keyword(s):  
Public Health ◽  
Influenza Vaccination ◽  
Vaccination Programme ◽  
Social Care ◽  
Severe Disease ◽  
Influenza Vaccines ◽  
Practice Nurses ◽  
Department Of Health ◽  
Main Challenge ◽  
Health And Social Care

The influenza immunisation season of 2020/21 was very challenging for practice nurses involved in delivering the programme. The main challenge was delivering the programme while coping with the difficulties of ensuring venues and practices were operating safely with the aim of reducing the risk of transmission of the SARS-CoV-2 virus. There has been comprehensive guidance from the Department of Health and Social Care (DHSC), Public Health England (PHE) and the Royal Colleges to support vaccination providers this year. Additionally, the vaccination programme was expanded to include more patients who are at risk of severe disease from influenza and SARS-CoV-2. This expanded programme is likely to continue in 2021/22 and guidance and directives on influenza vaccines for use in the programme are expected soon.

scholarly journals Development of a Process and Infrastructure to Outreach Stakeholders for Capturing Healthcare System Stress in Emergency Response Situations

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Taylor Read ◽  
Elizabeth White ◽  
J Perren Cobb ◽  
Perry Mar ◽  
Mahesh Shanmugam ◽  
...  
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Real Time ◽  
Healthcare System ◽  
Public Health Emergency ◽  
Time Data ◽  
National Network ◽  
Blind Review ◽  
Geographical Regions ◽  
Real Time Data

Real time data provided by frontline clinicians could be used to direct immediate resources during a public health emergency and inform increased preparedness for future events.  The [group name removed for blind review], a group of expert critical care and emergency medicine physicians at various academic medical centers across the US, aims to enhance the national capability of rapid electronic data collection, along with analysis and dissemination of findings. To achieve these aims, [group name removed for blind review] created a process for real-time data capture that relies on a curated and engaged network of clinical providers from various geographical regions to respond to short online “Pulse” queries about healthcare system stress. During a period of three years, five queries were created and distributed. The first two queries were used to develop and validate the data collection infrastructure.Results are reported for the last three queries between June 2015 and March 2016. Response rates consistently ranged from 39% to 42%. Our team demonstrated that our system and processes were ready for creation and rapid dissemination of episodic queries for rapid data collection, transmittal, and analysis through a curated national network of clinician responders during a public health emergency. [group name removed for blind review] aims to further increase the response rate through additional engagement efforts within the network, to continue to grow the clinician responder database, and to optimize additional query content. 

scholarly journals CDC’s Multiple Approaches to Safeguard the Health, Safety, and Resilience of Ebola Responders

2019 ◽  
Vol 35 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Richard W. Klomp ◽  
Laurie Jones ◽  
Emi Watanabe ◽  
William W. Thompson
Keyword(s):  
Public Health ◽  
Well Being ◽  
Clinical Decision ◽  
Health Clinic ◽  
Small Subset ◽  
Screening Process ◽  
Team Members ◽  
Health Safety ◽  
Incident Command ◽  
Increased Risk

AbstractOver 27,000 people were sickened by Ebola and over 11,000 people died between March of 2014 and June of 2016. The US Centers for Disease Control and Prevention (CDC; Atlanta, Georgia USA) was one of many public health organizations that sought to stop this outbreak. This agency deployed almost 2,000 individuals to West Africa during that timeframe. Deployment to these countries exposed these individuals to a wide variety of dangers, stressors, and risks.Being concerned about the at-risk populations in Africa, and also the well-being of its professionals who willingly deployed, the CDC did several things to help safeguard the health, safety, and resilience of these team members before, during, and after deployment.The accompanying special report highlights innovative pre-deployment training initiatives, customized screening processes, and post-deployment outreach efforts intended to protect and support the public health professionals fighting Ebola. Before deploying, the CDC team members were expected to participate in both internally-created and externally-provided trainings. These ranged from pre-deployment briefings, to Preparing for Work Overseas (PFWO) and Public Health Readiness Certificate Program (PHRCP) courses, to Incident Command System (ICS) 100, 200, and 400 courses.A small subset of non-clinical deployers also participated in a three-day training designed in collaboration with the Center for the Study of Traumatic Stress (CSTS; Bethesda, Maryland USA) to train individuals to assess and address the well-being and resilience of themselves and their teammates in the field during a deployment. Participants in this unique training were immersed in a Virtual Reality Environment (VRE) that simulated deployment to one of seven different types of emergencies.The CDC leadership also requested a pre-deployment screening process that helped professionals in the CDC’s Occupational Health Clinic (OHC) determine whether or not individuals were at an increased risk of negative outcomes by participating in a rigorous deployment at that time.When deployers returned from the field, they received personalized invitations to participate in a voluntary, confidential, post-deployment operational debriefing one-on-one or in a group.Implementing these approaches provided more information to clinical decision makers about the readiness of deployers. It provided deployers with a greater awareness of the kinds of challenges they were likely to face in the field. The post-deployment outreach efforts reminded staff that their contributions were appreciated and there were resources available if they needed help processing any of the potentially-traumatizing things they may have experienced.

scholarly journals Introduction and community transmission of SARS-CoV-2 lineage A.2.5 in Florida with novel spike INDELS

2021 ◽  
Author(s):  
Sarah E. Schmedes ◽  
Taj Azarian ◽  
Eleonora Cella ◽  
Jessy Motes ◽  
Omer Tekin ◽  
...  
Keyword(s):  
Public Health ◽  
Healthcare Facility ◽  
The State ◽  
Antibody Neutralization ◽  
Public Health Response ◽  
Department Of Health ◽  
Florida Department ◽  
Health Response ◽  
Geographical Regions ◽  
Community Transmission

AbstractSARS-CoV-2 (SC2) variants of concern (VOC) continue to emerge and spread globally, threatening the use of monoclonal antibody therapies and vaccine effectiveness. Several mutations in the SC2 spike glycoprotein have been associated with reduction in antibody neutralization. Genomic surveillance of SC2 variants has been imperative to inform the public health response regarding the use of clinical therapies in specific jurisdictions based on the proportion of particular variants (e.g., Gamma (P.1)) in a region. Florida Department of Health Bureau of Public Health Laboratories (BPHL) performs tiled-amplicon whole genome sequencing for baseline and targeted surveillance of SC2 isolates in Florida from clinical specimens collected from county health departments and hospitals throughout the state. Here, we describe the introduction of SC2 lineage A.2.5 in Florida, which contains S:L452R (a substitution of therapeutic concern) and two novel Spike INDELS, the deletion of 141-143 and ins215AGY, with unknown implications on immune response. The A.2.5 lineage was first detected in Florida among an outbreak at a healthcare facility in January 2021, and subsequent A.2.5 isolates were detected across all geographical regions throughout the state. A time-scaled maximum clade credibility phylogeny determined there were at least eight separate introductions of A.2.5 in the state. The time of introduction of a monophyletic Florida clade was established to be December 2020. The Spike INDELS were determined to reside in the N-terminal domain, a region associated with antibody neutralization. As community transmission of SARS-CoV-2 in Florida continues, genomic surveillance of circulating variants in Florida and the detection of emerging variants are critical for informing public health response to COVID-19.

scholarly journals The case for tracking misinformation the way we track disease

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser
Keyword(s):  
Public Health ◽  
Real Time ◽  
Supervised Machine Learning ◽  
Disease Spread ◽  
The Public ◽  
Monitoring Tools ◽  
Core Function ◽  
Public Health Organizations ◽  
Front Line Workers ◽  
Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

Accuracy of Medical Examiner’s Assessment for Near–Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017–February 2018

2021 ◽  
pp. 003335492110084
Author(s):  
Kirsten Vannice ◽  
Julia Hood ◽  
Nicole Yarid ◽  
Meagan Kay ◽  
Richard Harruff ◽  
...  
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Real Time ◽  
Negative Predictive Value ◽  
Drug Overdose ◽  
Predictive Value ◽  
Death Certificate ◽  
King County ◽  
Public Health Response ◽  
Drug Overdoses

Objectives Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near–real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring. Methods We analyzed data on deaths in the King County (Washington) Medical Examiner’s Office (KCMEO) jurisdiction that occurred during March 1, 2017–February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs’ classification by using the final death certificate as the gold standard. Results KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event. Conclusions King County MEs’ probable overdose classification provides a near–real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic.

scholarly journals Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ania Syrowatka ◽  
Masha Kuznetsova ◽  
Ava Alsubai ◽  
Adam L. Beckman ◽  
Paul A. Bain ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Real Time ◽  
Scoping Review ◽  
Grey Literature ◽  
Use Cases ◽  
Outbreak Detection ◽  
Response To Treatment ◽  
Pandemic Preparedness ◽  
Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Expanding the concept of the professional integrity of obstetrics during a public health emergency

2020 ◽  
Vol 48 (5) ◽  
pp. 435-437 ◽  
Author(s):  
Frank A. Chervenak ◽  
Amos Grünebaum ◽  
Eran Bornstein ◽  
Shane Wasden ◽  
Adi Katz ◽  
...  
Keyword(s):  
Public Health ◽  
New York ◽  
Clinical Judgment ◽  
Ethical Judgment ◽  
Population Based ◽  
Public Health Emergency ◽  
Ethical Challenge ◽  
Professional Integrity ◽  
Increased Risk ◽  
Obstetric Services

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has placed great demands on many hospitals to maximize their capacity to care for affected patients. The requirement to reassign space has created challenges for obstetric services. We describe the nature of that challenge for an obstetric service in New York City. This experience raised an ethical challenge: whether it would be consistent with professional integrity to respond to a public health emergency with a plan for obstetric services that would create an increased risk of rare maternal mortality. We answered this question using the conceptual tools of professional ethics in obstetrics, especially the professional virtue of integrity. A public health emergency requires frameshifting from an individual-patient perspective to a population-based perspective. We show that an individual-patient-based, beneficence-based deliberative clinical judgment is not an adequate basis for organizational policy in response to a public health emergency. Instead, physicians, especially those in leadership positions, must frameshift to population-based clinical ethical judgment that focuses on reduction of mortality as much as possible in the entire population of patients served by a healthcare organization.

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