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. Our approach involved: (1) use of geospatial analytics to identify testing locations positioned to optimize access; (2) community engagement to ensure sites were in trusted places; and (3) tracking of data over time to facilitate ongoing improvement. Between August 2020 and December 2021, more than 65 000 tests were completed for nearly 46 000 individuals at community-based testing sites. These methods could have application beyond COVID-19 and our region.

Addressing the Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Hematopoietic Cell Transplantation: Learning Networks as a Means for Sharing Best Practices.

The full impact of the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), on the field of hematopoietic cell transplantation (HCT) is unknown. This perspective paper reviews the following: current COVID-19 epidemiology, diagnosis, and potential therapies; care considerations unique to HCT recipients; and the concept of a learning network to assimilate emerging guidelines and best practices and to optimize patient outcomes through facilitating shared learning and experience across transplantation centers.

Association Between Statewide School Closure and COVID-19 Incidence and Mortality in the US.

Importance: In the US, states enacted nonpharmaceutical interventions, including school closure, to reduce the spread of coronavirus disease 2019 (COVID-19). All 50 states closed schools in March 2020 despite uncertainty if school closure would be effective. Objective: To determine if school closure and its timing were associated with decreased COVID-19 incidence and mortality. Design, Setting, and Participants: US population-based observational study conducted between March 9, 2020, and May 7, 2020, using interrupted time series analyses incorporating a lag period to allow for potential policy-associated changes to occur. To isolate the association of school closure with outcomes, state-level nonpharmaceutical interventions and attributes were included in negative binomial regression models. States were examined in quartiles based on state-level COVID-19 cumulative incidence per 100 000 residents at the time of school closure. Models were used to derive the estimated absolute differences between schools that closed and schools that remained open as well as the number of cases and deaths if states had closed schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile. Exposures: Closure of primary and secondary schools. Main Outcomes and Measures: COVID-19 daily incidence and mortality per 100 000 residents. Results: COVID-19 cumulative incidence in states at the time of school closure ranged from 0 to 14.75 cases per 100 000 population. School closure was associated with a significant decline in the incidence of COVID-19 (adjusted relative change per week, -62% [95% CI, -71% to -49%]) and mortality (adjusted relative change per week, -58% [95% CI, -68% to -46%]). Both of these associations were largest in states with low cumulative incidence of COVID-19 at the time of school closure. For example, states with the lowest incidence of COVID-19 had a -72% (95% CI, -79% to -62%) relative change in incidence compared with -49% (95% CI, -62% to -33%) for those states with the highest cumulative incidence. In a model derived from this analysis, it was estimated that closing schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile was associated with 128.7 fewer cases per 100 000 population over 26 days and with 1.5 fewer deaths per 100 000 population over 16 days. Conclusions and Relevance: Between March 9, 2020, and May 7, 2020, school closure in the US was temporally associated with decreased COVID-19 incidence and mortality; states that closed schools earlier, when cumulative incidence of COVID-19 was low, had the largest relative reduction in incidence and mortality. However, it remains possible that some of the reduction may have been related to other concurrent nonpharmaceutical interventions.

An Australian version of the acceptable noise level test and its predictive value for successful hearing aid use in an older population.

OBJECTIVE: The acceptable noise level (ANL), a measure of noise tolerance, has been proposed as a predictor for successful hearing aid use. The aims of this study were to obtain normative data, and to evaluate the clinical feasibility and predictive value of an Australian version of the ANL test in an older population. DESIGN: Repeated ANL measurements were presented diotically using earphones. All participants provided demographic information and hearing aid owners were asked about their aid use. STUDY SAMPLE: A total of 290 older adults were assessed; 166 participants had a hearing impairment and 96 owned a hearing aid. RESULTS: The mean ANL was lower than previously reported. While age and gender had no effect on ANL, a significant, but weak, correlation was found between ANL and hearing loss. The test-retest reliability showed the results were clinically unreliable. In addition, the ANL did not predict hours or pattern of hearing aid use. CONCLUSIONS: While the Australian ANL test showed several similar characteristics to previous versions of the test, low test-retest reliability raised questions about its clinical value as a predictor for long-term hearing aid use.

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.

A regional learning health system of congregate care facilities for COVID-19 response.

Introduction: The COVID-19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations. Methods: We formed a learning health system of CC facilities in our 14-county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID-19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID-19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs. Results: Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID-19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages. Conclusions: A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.

Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes.

OBJECTIVES: We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS: We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS: We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION: Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION: We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

Impact of the COVID-19 pandemic on hospitalizations of children with neurologic impairment.

INTRODUCTION: Children with neurologic impairment (NI) are frequently hospitalized for infectious and noninfectious illnesses. The early period of the COVID-19 pandemic was associated with overall lower pediatric hospitalization rates, particularly for respiratory infections, but the effect on utilization for children with NI is unknown. METHOD: This multicenter retrospective cohort study included hospitalizations of children 1-18 years of age with NI diagnosis codes from 49 children's hospitals. We calculated the percent change in the median weekly hospitalization volumes and the hospitalization resource intensity score (H-RISK), comparing the early-COVID era (March 15, 2020 to December 31, 2020) with the pre-COVID era (same timeframe of 2017-2019). Percent change was calculated over the entire study period as well as within three seasonal time periods (spring, summer, and fall/winter). Differences between infectious and noninfectious admission diagnoses were also examined. RESULTS: Compared with the pre-COVID era, there was a 14.4% decrease (interquartile range [IQR]: -33.8, -11.7) in the weekly median number of hospitalizations in the early-COVID era; the weekly median H-RISK score was 11.7% greater (IQR: 8.9, 14.9). Hospitalizations decreased for both noninfectious (-11.6%, IQR: -30.0, -8.0) and infectious (-35.5%, IQR: -51.1, -31.3) illnesses in the early-COVID era. This decrease was the largest in spring 2020 and continued throughout 2020. CONCLUSIONS: For children with NI, there was a substantial and significant decrease in hospitalizations for infectious and noninfectious diagnoses but an increase in illness severity during the early-COVID era compared with the pre-COVID era. Our data suggest a need to reconsider current thresholds for hospitalization and identify opportunities to support and guide families through certain illnesses without hospitalization.

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.

Neurodiversity, Networks, and Narratives: Exploring Intimacy and Expressive Freedom in the Time of Covid-19.

The Narratives of Neurodiversity Network (NNN) is a neurodivergent academic, creative, and educator collective that came together with allies during the Covid-19 pandemic to create a network centred around emerging narratives about neurodiversity and exploring new ways of learning and socialising. The network focuses on exploring the roles of written, spoken, and visual narratives across cultural locations about neuro-atypical experiences in generating improved agency and self-advocacy for those who have been subject to pathologization through neuro-normativity and intersecting oppression. During the last year, widening access to digital platforms has provided a space to explore these issues outside of traditional academic spaces. We run a monthly "Salon," our mixed-media "reading, listening, and watching" group, in an effort to find positive representation within contemporary culture. Discussions have moved beyond mimesis and into a consideration of how narrative and storyworlds can question the supposed naturalness of certain ways of being in and perceiving the world. This article interrogates the network's core principles of nonhierarchical co-production, including the roles of creativity, community, identity, and emancipatory research which were animated by the new techno-social context. We consider the cultural lives of neurodiversity in the West and beyond, including ethical and aesthetic dimensions. We share a faith in the power of storytelling to inform new social identities for neurodivergent people and to inform scientific understandings of atypical cognition. In exploring this, we speak through a porous first-person plural narrator, to unsettle the idea that there is a hegemonic "we" speaking on behalf of all neurodivergent people.

Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data.

BACKGROUND: Individual children's hospitals care for a small number of patients with multisystem inflammatory syndrome in children (MIS-C). Administrative databases offer an opportunity to conduct generalizable research; however, identifying patients with MIS-C is challenging. METHODS: We developed and validated algorithms to identify MIS-C hospitalizations in administrative databases. We developed 10 approaches using diagnostic codes and medication billing data and applied them to the Pediatric Health Information System from January 2020 to August 2021. We reviewed medical records at 7 geographically diverse hospitals to compare potential cases of MIS-C identified by algorithms to each participating hospital's list of patients with MIS-C (used for public health reporting). RESULTS: The sites had 245 hospitalizations for MIS-C in 2020 and 358 additional MIS-C hospitalizations through August 2021. One algorithm for the identification of cases in 2020 had a sensitivity of 82%, a low false positive rate of 22%, and a positive predictive value (PPV) of 78%. For hospitalizations in 2021, the sensitivity of the MIS-C diagnosis code was 98% with 84% PPV. CONCLUSION: We developed high-sensitivity algorithms to use for epidemiologic research and high-PPV algorithms for comparative effectiveness research. Accurate algorithms to identify MIS-C hospitalizations can facilitate important research for understanding this novel entity as it evolves during new waves.

Epidemiology and Severity of Illness of MIS-C and Kawasaki Disease During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Multisystem inflammatory syndrome in children (MIS-C) is a novel, severe condition following severe acute respiratory syndrome coronavirus 2 infection. Large epidemiologic studies comparing MIS-C to Kawasaki disease (KD) and evaluating the evolving epidemiology of MIS-C over time are lacking. We sought to understand the illness severity of MIS-C compared with KD and evaluate changes in MIS-C illness severity over time during the coronavirus disease 2019 pandemic compared with KD. METHODS: We included hospitalizations of children with MIS-C and KD from April 2020 to May 2022 from the Pediatric Health Information System administrative database. Our primary outcome measure was the presence of shock, defined as the use of vasoactive/inotropic cardiac support or extracorporeal membrane oxygenation. We examined the volume of MIS-C and KD hospitalizations and the proportion of hospitalizations with shock over time using 2-week intervals. We compared the proportion of hospitalizations with shock in MIS-C and KD patients over time using generalized estimating equations adjusting for hospital clustering and age, with time as a fixed effect. RESULTS: We identified 4868 hospitalizations for MIS-C and 2387 hospitalizations for KD. There was a higher proportion of hospitalizations with shock in MIS-C compared with KD (38.7% vs 5.1%). In our models with time as a fixed effect, we observed a significant decrease in the odds of shock over time in MIS-C patients (odds ratio 0.98, P < .001) but not in KD patients (odds ratio 1.00, P = .062). CONCLUSIONS: We provide further evidence that MIS-C is a distinct condition from KD. MIS-C was a source of lower morbidity as the pandemic progressed.

Genomic diagnosis and care co-ordination for monogenic inflammatory bowel disease in children and adults: consensus guideline on behalf of the British Society of Gastroenterology and British Society of Paediatric Gastroenterology, Hepatology and Nutrition.

Genomic medicine enables the identification of patients with rare or ultra-rare monogenic forms of inflammatory bowel disease (IBD) and supports clinical decision making. Patients with monogenic IBD frequently experience extremely early onset of treatment-refractory disease, with complex extraintestinal disease typical of immunodeficiency. Since more than 100 monogenic disorders can present with IBD, new genetic disorders and variants are being discovered every year, and as phenotypic expression of the gene defects is variable, adaptive genomic technologies are required. Monogenic IBD has become a key area to establish the concept of precision medicine. Clear guidance and standardised, affordable applications of genomic technologies are needed to implement exome or genome sequencing in clinical practice. This joint British Society of Gastroenterology and British Society of Paediatric Gastroenterology, Hepatology and Nutrition guideline aims to ensure that testing resources are appropriately applied to maximise the benefit to patients on a national scale, minimise health-care disparities in accessing genomic technologies, and optimise resource use. We set out the structural requirements for genomic medicine as part of a multidisciplinary team approach. Initiation of genomic diagnostics should be guided by diagnostic criteria for the individual patient, in particular the age of IBD onset and the patient's history, and potential implications for future therapies. We outline the diagnostic care pathway for paediatric and adult patients. This guideline considers how to handle clinically actionable findings in research studies and the impact of consumer-based genomics for monogenic IBD. This document was developed by multiple stakeholders, including UK paediatric and adult gastroenterology physicians, immunologists, transplant specialists, clinical geneticists, scientists, and research leads of UK genetic programmes, in partnership with patient representatives of several IBD and rare disease charities.

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.

Toward an ontology of collaborative learning healthcare systems.

Objective: To establish a basis for a domain ontology - a formal, explicit specification of a shared conceptualization - of collaborative learning healthcare systems (CLHSs) in order to facilitate measurement, explanation, and improvement. Methods: We adapted the "Methontology" approach to begin building an ontology of CLHSs. We specified the purpose of an ontology, acquired domain knowledge via literature review, conceptualized a common framework of CLHSs using a grounded approach, refined these concepts based on expert panel input, and illustrated concept application via four cases. Results: The set of concepts identified as important to include in an ontology includes goals, values, structure, actors, environment, and products. To establish this set of concepts, we gathered input from content experts in two ways. First, expert panel methods were used to elicit feedback on these concepts and to test the elicitation of terms for the vocabulary of the Values concept. Second, from these discussions we developed a mapping exercise to test the intuitiveness of the concepts, requesting that network leaders from four CLHSs complete a mapping exercise to associate characteristics of their networks with the high-level concepts, building the vocabulary for each concept in a grounded fashion. We also solicited feedback from these participants on the experience of completing the mapping exercise, finding that the exercise is acceptable and could aid in CLHS development and collaboration. Respondents identified opportunities to improve the operational definitions of each concept to ensure that corresponding vocabularies are distinct and non-overlapping. Discussion: Our results provide a foundation for developing a formal, explicit shared conceptualization of CLHSs. Once developed, such a tool can be useful for measurement, explanation, and improvement. Further work, including alignment to a top-level ontology, expanding the vocabulary, and defining relations between vocabulary is required to formally build out an ontology for these uses.

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%).

Quality Improvement in Hematopoietic Stem Cell Transplant and Cellular Therapy: Using the Model for Improvement to impact Outcomes.

Quality improvement and quality assurance form a complementary and independent relationship. Quality assurance measures compliance against industry standards using audits, whereas quality improvement is a continuous process focused on processes and systems that can improve care. The Model for Improvement is a robust quality improvement tool that transplant and cellular therapy teams can use to redesign healthcare processes. The Model for Improvement uses several components addressed in sequence to organize and critically evaluate improvement activities. Unlike other health sciences clinical research, quality improvement projects, and research are based on dynamic hypotheses that develop into observable, serial tests of change with continuous collection and feedback of performance data to stakeholders.

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.