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. Methods: In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences. Results: Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool. Conclusions: There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.

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. This study employs comprehensive search strategies with data extraction, meta-analysis and mapping to help address gaps in the understanding of the risks of carbapenem and polymyxin resistance in the nations of Africa. METHODS: Three comprehensive Boolean searches were constructed and utilized to query scientific and medical databases as well as grey literature sources through the end of 2019. Search results were screened to exclude irrelevant results and remaining studies were examined for relevant information regarding carbapenem and/or polymyxin(s) susceptibility and/or resistance amongst E. coli and Klebsiella isolates from humans. Such data and study characteristics were extracted and coded, and the resulting data was analyzed and geographically mapped. RESULTS: Our analysis yielded 1341 reports documenting carbapenem resistance in 40 of 54 nations. Resistance among E. coli was estimated as high (> 5%) in 3, moderate (1-5%) in 8 and low (< 1%) in 14 nations with at least 100 representative isolates from 2010 to 2019, while present in 9 others with insufficient isolates to support estimates. Carbapenem resistance was generally higher among Klebsiella: high in 10 nations, moderate in 6, low in 6, and present in 11 with insufficient isolates for estimates. While much less information was available concerning polymyxins, we found 341 reports from 33 of 54 nations, documenting resistance in 23. Resistance among E. coli was high in 2 nations, moderate in 1 and low in 6, while present in 10 with insufficient isolates for estimates. Among Klebsiella, resistance was low in 8 nations and present in 8 with insufficient isolates for estimates. The most widespread associated genotypes were, for carbapenems, blaOXA-48, blaNDM-1 and blaOXA-181 and, for polymyxins, mcr-1, mgrB, and phoPQ/pmrAB. Overlapping carbapenem and polymyxin resistance was documented in 23 nations. CONCLUSIONS: While numerous data gaps remain, these data show that significant carbapenem resistance is widespread in Africa and polymyxin resistance is also widely distributed, indicating the need to support robust AMR surveillance, antimicrobial stewardship and infection control in a manner that also addresses broader animal and environmental health dimensions.

Event-based internet biosurveillance: relation to epidemiological observation.

BACKGROUND: The World Health Organization (WHO) collects and publishes surveillance data and statistics for select diseases, but traditional methods of gathering such data are time and labor intensive. Event-based biosurveillance, which utilizes a variety of Internet sources, complements traditional surveillance. In this study we assess the reliability of Internet biosurveillance and evaluate disease-specific alert criteria against epidemiological data. METHODS: We reviewed and compared WHO epidemiological data and Argus biosurveillance system data for pandemic (H1N1) 2009 (April 2009 - January 2010) from 8 regions and 122 countries to: identify reliable alert criteria among 15 Argus-defined categories; determine the degree of data correlation for disease progression; and assess timeliness of Internet information. RESULTS: Argus generated a total of 1,580 unique alerts; 5 alert categories generated statistically significant (p < 0.05) correlations with WHO case count data; the sum of these 5 categories was highly correlated with WHO case data (r = 0.81, p < 0.0001), with expected differences observed among the 8 regions. Argus reported first confirmed cases on the same day as WHO for 21 of the first 64 countries reporting cases, and 1 to 16 days (average 1.5 days) ahead of WHO for 42 of those countries. CONCLUSION: Confirmed pandemic (H1N1) 2009 cases collected by Argus and WHO methods returned consistent results and confirmed the reliability and timeliness of Internet information. Disease-specific alert criteria provide situational awareness and may serve as proxy indicators to event progression and escalation in lieu of traditional surveillance data; alerts may identify early-warning indicators to another pandemic, preparing the public health community for disease events.

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. The COVID-19 pandemic has had numerous secondary impacts on patients undergoing HSCT and the healthcare delivery systems involved in providing complex care to HSCT recipients. Institutions must identify these influences on outcomes and adjust accordingly to maintain and improve outcomes for the transplantation and cellular therapy community.

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. Methods: In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review. Results: Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures. Conclusions: Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).

Potential effects of Rift Valley fever in the United States.

Rift Valley fever virus (RVFV) has been the cause of disease outbreaks throughout Africa and the Arabian Peninsula, and the infection often results in heavy economic costs through loss of livestock. If RVFV, which is common to select agent lists of the US Department of Health and Human Services and the US Department of Agriculture, entered the United States, either by accidental or purposeful means, the effects could be substantial. A group of subject matter experts met in December 2009 to discuss potential implications of an introduction of RVF to the United States and review current modeling capabilities. This workshop followed a similar meeting held in April 2007. This report summarizes the 2 workshop proceedings. Discussions primarily highlighted gaps in current economic and epidemiologic RVF models as well as gaps in the overall epidemiology of the virus.

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. Previously described Collaborative Learning Health Systems (CLHSs) are a promising approach to outcomes improvement. In order to fully realize this promise, a deeper understanding of this phenomenon is necessary. METHODS: We drew on our experience over the past decade with CLHSs as well as qualitative literature review to answer three questions: What kind of phenomena are CLHSs? and what is an appropriate scientific approach? How might we frame CLHSs conceptually? What are potential mechanisms of action? RESULTS: CLHSs are complex adaptive systems in which all stakeholders are able to collaborate, at scale, to create and share resources to satisfy a variety of needs. This is accomplished by providing infrastructure and services that enable stakeholders to act on their inherent motivations. This framing has implications for both research and practice. CONCLUSION: Articulating this framework and potential mechanisms of action should facilitate research to test and refine hypotheses as well as guide practice to develop and optimize this promising approach to improving healthcare systems.

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. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity. METHODS: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes. RESULTS: The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. CONCLUSIONS: We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.

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. OBJECTIVES: We describe efforts in ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease (IBD), to increase the number of patients and families creating and accessing IKK, and the challenges faced in that process. METHODS: We applied tactics such as outreach through trusted messengers, community organizing, and digital outreach such as sharing resources on our website, via social media, and email to increase the number of people producing, able to access, and accessing IKK. We applied an existing measurement system to track our progress and supplemented this with community feedback. RESULTS: In August of 2017 we identified and began measuring specific actions to track community growth. The number of patients and families producing IKK has increased by a factor of 2.7, using resources has increased by a factor of 4.1 and aware of resources as increased by a factor of 4.0. We identified challenges to measurement and scaling. CONCLUSIONS: It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.

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. INTRODUCTION: We developed a categorical classification scheme for engagement in ImproveCareNow. Each tier is defined in terms of observable individual behaviors. When an individual completes one or more qualifying behavior, s/he is classified as engaged at that tier. Individuals are entered into a database, which is accessible to care centers throughout the ImproveCareNow network. Database records include fields for individual name, behavior type, time, place, and level of engagement. RESULTS: The resulting system is employed at 79 ImproveCareNow care centers in the United States. The system recognizes four levels of engagement. Behaviors are recorded in a managed vocabulary and recorded in an online database. The database is queried weekly for individual engagement behaviors, which are tracked longitudinally. Center- and network-level statistics are generated and disseminated to stakeholders. CONCLUSION: It is possible to monitor longitudinal engagement in a collaborative learning health system, thereby charting progress toward engagement goals and enabling quantitative evaluation of interventions aimed at increasing engagement.

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. METHODS: PCORnet networks standardized EHR-derived data to a common data model. In 13 health systems from 2 networks for 2015, we implemented the National Quality Forum measures: % children with sickle cell anemia who received a transcranial Doppler; % children on antipsychotics who had metabolic screening; and % pediatric acute otitis media with amoxicillin prescribed. Manual chart review assessed measure accuracy. RESULTS: Only 39% (N = 2,923) of 7,278 children on antipsychotics received metabolic screening (range: 20%-54%). If the measure indicated screening was performed, the chart agreed 88% of the time [95% confidence interval (CI): 81%-94%]; if it indicated screening was not done, the chart agreed 86% (95% CI: 78%-93%). Only 69% (N = 793) of 1,144 children received transcranial Doppler screening (range across sites: 49%-88%). If the measure indicated screening was performed, the chart agreed 98% of the time (95% CI: 94%-100%); if it indicated screening was not performed, the chart agreed 89% (95% CI: 82%-95%). For acute otitis media, chart review identified many qualifying cases missed by the National Quality Forum measure, which excluded a common diagnostic code. CONCLUSIONS: Measures of healthcare quality developed using EHR-derived data were valid and identified wide variation among network sites. This data can facilitate the identification and spread of best practices.

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. OBJECTIVE: To describe and measure implementation of elements of the actor-oriented architecture in an existing Collaborative Learning Health System. METHODS: We used the case of ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease, founded in 2006. We traced several network-level indicators of actor-oriented architecture between 2010 and 2016. RESULTS: We identified measures of actors, the commons, and ways that have made it easier for network member sites to participate. These indicators show ImproveCareNow has made changes in the three elements of the actor-oriented architecture over time. CONCLUSION: It is possible to measure the implementation of an actor-oriented architecture in a CLHS. The elements of the actor-oriented architecture may provide a conceptual framework for their development and optimization. Metrics such as those described here may be actionable indicators of the "health of the system."

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. Results demonstrate that although parents may have little experience using data on health care quality for decision making, they affirmed its potential value. HSLs expressed the need for high-confidence, validated metrics. They also perceived barriers to achieving meaningful metrics but recognized that mining data directly from the EHR could overcome those barriers. Parents and HSLs need high-confidence health care quality data to support decision making.

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. To improve our understanding of the challenge, we utilised broad strategies to search for and analyse data on carbapenem and colistin resistance among Escherichia coli and Klebsiella in Southeast Asia. We found 258 studies containing 526 unique reports and document carbapenem-resistant E. coli and Klebsiella in 8 and 9 of 11 nations, respectively. We estimated carbapenem resistance proportions through meta-analysis of extracted data for nations with ≥100 representative isolates. Estimated resistance among Klebsiella was high (>5%) in four nations (Indonesia, Philippines, Thailand and Vietnam), moderate (1-5%) in two nations (Malaysia and Singapore) and low (<1%) in two nations (Cambodia and Brunei). For E. coli, resistance was generally lower but was high in two of seven nations with ≥100 isolates (Indonesia and Myanmar). The most common carbapenemases were NDM metallo-ß-lactamases and OXA ß-lactamases. Despite sparse data, polymyxin resistance was documented in 8 of 11 nations, with mcr-1 being the predominant genotype. Widespread presence of carbapenem and polymyxin resistance, including their overlap in eight nations, represents a continuing risk and increases the threat of infections resistant to both classes. These findings, and remaining data gaps, highlight the urgent need for sufficiently-resourced robust antimicrobial resistance surveillance.

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. and Escherichia coli from the World Health Organization (WHO) Africa Region. Core data elements were extracted for meta-analysis and mapping. Despite sparse data in existing reviews, 180 documents including 314 reports on susceptibility of E. coli and/or Klebsiella were located, providing information on 31 (66%) of 47 nations. Carbapenem-resistant E. coli or Klebsiella were identified in 22 (71%) of these 31 countries. Crude resistance proportions were estimated for nations with >100 representative isolates. Median resistance among E. coli was <1% in 11 (61%) of 18 nations meeting criteria, 1-5% in 6 nations (33%) and >5% in 1 nation (6%). For Klebsiella spp., corresponding figures were <1% in 10 (67%) of 15 nations, 1-5% in 3 nations (20%) and >5% in 2 nations (13%). Comprehensive, customised search strategies with analysis and mapping of defined data elements provide an enhanced view of carbapenem-resistant E. coli and Klebsiella in Africa. These CRE are widely distributed and are generally present at low to moderate levels. Whilst use of diverse and largely clinically derived data has limitations and cannot substitute for surveillance, it can enhance situational awareness. The approaches utilised can support improved risk understanding and prioritisation and may be applied to other micro-organisms and areas where surveillance remains inadequate.

Methicillin-resistant Staphylococcus aureus infection and colonization among hospitalized prisoners.

We assessed methicillin-resistant Staphylococcus aureus (MRSA) infection and colonization in hospitalized prisoners. Of 434 admission surveillance cultures, 58 (13%) were positive for MRSA. The sensitivity of admission surveillance cultures of samples from the anterior nares was 72% and increased to 84% when the calculation included cultures of wound samples. Hospitalized prisoners are at high risk for MRSA infection and colonization, and surveillance should include cultures of nares and wound samples.

Value of performing active surveillance cultures on intensive care unit discharge for detection of methicillin-resistant Staphylococcus aureus.

OBJECTIVE: To quantify the value of performing active surveillance cultures for detection of methicillin-resistant Staphylococcus aureus (MRSA) on intensive care unit (ICU) discharge. DESIGN: Prospective cohort study. SETTING: Medical ICU (MICU) and surgical ICU (SICU) of a tertiary care hospital. PARTICIPANTS: We analyzed data on adult patients who were admitted to the MICU or SICU between January 17, 2001, and December 31, 2004. All participants had a length of ICU stay of at least 48 hours and had surveillance cultures of anterior nares specimens performed on ICU admission and discharge. Patients who had MRSA-positive clinical cultures in the ICU were excluded. RESULTS: Of 2,918 eligible patients, 178 (6%) were colonized with MRSA on ICU admission, and 65 (2%) acquired MRSA in the ICU and were identified by results of discharge surveillance cultures. Patients with MRSA colonization confirmed by results of discharge cultures spent 853 days in non-ICU wards after ICU discharge, which represented 27% of the total number of MRSA colonization-days during hospitalization in non-ICU wards for patients discharged from the ICU. CONCLUSIONS: Surveillance cultures of nares specimens collected at ICU discharge identified a large percentage of MRSA-colonized patients who would not have been identified on the basis of results of clinical cultures or admission surveillance cultures alone. Furthermore, these patients were responsible for a large percentage of the total number of MRSA colonization-days during hospitalization in non-ICU wards for patients discharged from the ICU.