The Test and Protect Program: A Data-Driven, Community-Engaged Approach to COVID-19 Testing Site Localization.

As the COVID-19 pandemic progressed, reliable, accessible, and equitable community-based testing strategies were sought that did not flood already overburdened hospitals and emergency departments. In Hamilton County, Ohio, home to ~800 000 people across urban, suburban, and rural areas, we sought to develop and optimize an accessible, equitable county-wide COVID-19 testing program. Using Coronavirus Aid, Relief, and Economic Security Act funding, multidisciplinary, multiorganization partners created the test and protect program to deliver safe, reliable testing in neighborhoods and organizations needing it most.

Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and preparation.

Introduction: The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic.

Review and analysis of the overlapping threats of carbapenem and polymyxin resistant E. coli and Klebsiella in Africa.

Background: Carbapenem-resistant Enterobacterales are among the most serious antimicrobial resistance (AMR) threats. Emerging resistance to polymyxins raises the specter of untreatable infections. These resistant organisms have spread globally but, as indicated in WHO reports, the surveillance needed to identify and track them is insufficient, particularly in less resourced countries.

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D.

Secondary Impact of the Coronavirus Disease 19 Pandemic on Patients and the Cellular Therapy Healthcare Ecosystem.

The Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted global health and healthcare delivery systems. To characterize the secondary effects of the COVID-19 pandemic and mitigation strategies used in the delivery of hematopoietic stem cell transplantation (HSCT) care, we performed a comprehensive literature search encompassing changes in specific donor collection, processing practices, patient outcomes, and patient-related concerns specific to HSCT and HSCT-related healthcare delivery. In this review, we summarize the available literature on the secondary impacts the COVID-19 pandemic on the fields of HSCT and cellular therapy.

Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality.

Introduction: Health systems spend $1.5 billion annually reporting data on quality, but efficacy and utility for benchmarking are limited due, in part, to limitations of data sources. Our objective was to implement and evaluate measures of pediatric quality for three conditions using electronic health record (EHR)-derived data.

A science of collaborative learning health systems.

Introduction: Improving the U.S. healthcare system and health outcomes is one of the most pressing public health challenges of our time.

Getting what is needed, when it's needed: Sharing information, knowledge, and know-how in a Collaborative Learning Health System.

Background: Collaborative Learning Health Systems (CLHS) improve outcomes in part by facilitating collaboration among all stakeholders. One way to facilitate collaboration is by creating conditions for the production and sharing of medical and non-medical resources (information, knowledge, and knowhow [IKK]) so anybody can get "what is needed, when it's needed" (WINWIN) to act in ways that improve health and healthcare. Matching resources to needs can facilitate accurate diagnosis, appropriate prescribing, answered questions, provision of emotional and social support, and uptake of innovations.

A collaborative learning health system agent-based model: Computational and face validity.

Introduction: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance.

Measuring engagement in a collaborative learning health system: The case of ImproveCareNow.

Background: Collaborative learning health systems have demonstrated improved outcomes for a range of different chronic conditions. Patient and healthcare provider engagement in these systems is thought to be associated with improved outcomes. We have adapted an observational framework to measure, and track over time, engagement in ImproveCareNow, a collaborative learning health system for children with inflammatory bowel disease.

Organizing for collaboration: An actor-oriented architecture in ImproveCareNow.

Background: Collaborative learning health systems (CLHSs) enable patients, clinicians, researchers, and others to collaborate at scale to improve outcomes and generate new knowledge. An organizational framework to facilitate this collaboration is the actor-oriented architecture, composed of (a) actors (people, organizations, and databases) with the values and abilities to self-organize; (b) a commons where they create and share resources; and (c) structures, protocols, and processes that facilitate multiactor collaboration. CLHSs may implement a variety of changes to strengthen the actor-oriented architecture and enable more actors to create and share resources.

Carbapenem and colistin resistance in Enterobacteriaceae in Southeast Asia: Review and mapping of emerging and overlapping challenges.

Carbapenem-resistant Enterobacteriaceae infections have spread globally, leaving polymyxins, including colistin, as 'last-resort treatments'. Emerging colistin resistance raises the spectre of untreatable infections. Despite this threat, data remain limited for much of the world, including Southeast Asia where only 3 of 11 nations submitted data on carbapenem and colistin resistance for recent World Health Organization (WHO) reports.

Use of EHR-Based Pediatric Quality Measures: Views of Health System Leaders and Parents.

Measures of health care quality are produced from a variety of data sources, but often, physicians do not believe these measures reflect the quality of provided care. The aim was to assess the value to health system leaders (HSLs) and parents of benchmarking on health care quality measures using data mined from the electronic health record (EHR). Using in-context interviews with HSLs and parents, the authors investigated what new decisions and actions benchmarking using data mined from the EHR may enable and how benchmarking information should be presented to be most informative.

Review and mapping of carbapenem-resistant Enterobacteriaceae in Africa: Using diverse data to inform surveillance gaps.

Carbapenem-resistant Enterobacteriaceae (CRE) are among the most difficult to treat emerging multidrug-resistant organisms. Major limitations exist in surveillance needed to address CRE, particularly in areas with inadequate resources. We utilised optimised strategies to search for data on carbapenem susceptibility of Klebsiella spp.

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital's primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our hospital's EDs and UCCs with ILI, as captured by ICD-9 codes. A Support Vector Machine classifier was applied to the set of tweets to remove those unlikely to be related to ILI.

Disease prediction models and operational readiness.

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition.

Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks.

Background: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.

Method And Findings: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail.

Recasting the theory of mosquito-borne pathogen transmission dynamics and control.

Mosquito-borne diseases pose some of the greatest challenges in public health, especially in tropical and sub-tropical regions of the world. Efforts to control these diseases have been underpinned by a theoretical framework developed for malaria by Ross and Macdonald, including models, metrics for measuring transmission, and theory of control that identifies key vulnerabilities in the transmission cycle. That framework, especially Macdonald's formula for R0 and its entomological derivative, vectorial capacity, are now used to study dynamics and design interventions for many mosquito-borne diseases.

Data-driven modeling to assess receptivity for Rift Valley Fever virus.

Rift Valley Fever virus (RVFV) is an enzootic virus that causes extensive morbidity and mortality in domestic ruminants in Africa, and it has shown the potential to invade other areas such as the Arabian Peninsula. Here, we develop methods for linking mathematical models to real-world data that could be used for continent-scale risk assessment given adequate data on local host and vector populations. We have applied the methods to a well-studied agricultural region of California with [Formula: see text]1 million dairy cattle, abundant and competent mosquito vectors, and a permissive climate that has enabled consistent transmission of West Nile virus and historically other arboviruses.