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Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Purpose Evidence of an association between intravenous contrast media (CM) and persistent renal dysfunction is lacking for patients with pre-existing acute kidney injury (AKI). This study was designed to determine the association between intravenous CM administration and persistent AKI in patients with pre-existing AKI. Methods A retrospective propensity-weighted and entropy-balanced observational cohort analysis of consecutive hospitalized patients [greater than or equal to] 18 years old meeting Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria for AKI at time of arrival to one of three emergency departments between 7/1/2017 and 6/30/2021 who did or did not receive intravenous CM. Outcomes included persistent AKI at hospital discharge and initiation of dialysis within 180 days of index encounter. Results Our analysis included 14,449 patient encounters, with 12.8% admitted to the intensive care unit (ICU). CM was administered in 18.4% of all encounters. AKI resolved prior to hospital discharge for 69.1%. No association between intravenous CM administration and persistent AKI was observed after unadjusted multivariable logistic regression modeling (OR 1; 95% CI 0.89-1.11), propensity weighting (OR 0.93; 95% CI 0.83-1.05), and entropy balancing (OR 0.94; 95% CI 0.83-1.05). Sub-group analysis in those admitted to the ICU yielded similar results. Initiation of dialysis within 180 days was observed in 5.4% of the cohort. An association between CM administration and increased risk of dialysis within 180 days was not observed. Conclusion Among patients with pre-existing AKI, contrast administration was not associated with either persistent AKI at hospital discharge or initiation of dialysis within 180 days. Current consensus recommendations for use of intravenous CM in patients with stable renal disease may also be applied to patients with pre-existing AKI., Author(s): Michael R. Ehmann [sup.1], Jonathon Mitchell [sup.1], Scott Levin [sup.1], Aria Smith [sup.1], Steven Menez [sup.2], Jeremiah S. Hinson [sup.1], Eili Y. Klein [sup.1] [sup.3] Author Affiliations: (1) grid.21107.35, [...]

Reducing inappropriate prescribing is key to mitigating antibiotic resistance, particularly in acute care settings. Clinicians' prescribing decisions are influenced by their judgments and actual or perceived patient expectations. Fuzzy trace theory predicts that patients and clinicians base such decisions on categorical gist representations that reflect the bottom-line understanding of information about antibiotics. However, due to clinicians' specialized training, the categorical gists driving clinicians' and patients' decisions might differ, which could result in mismatched expectations and inefficiencies in targeting interventions. We surveyed clinicians and patients from 2 large urban academic hospital emergency departments (EDs) and a sample of nonpatient subjects regarding their gist representations of antibiotic decisions, as well as relevant knowledge and expectations. Results were analyzed using exploratory factor analysis (EFA) and multifactor regression. In total, 149 clinicians (47% female; 74% white), 519 online subjects (45% female; 78% white), and 225 ED patients (61% female; 56% black) completed the survey. While clinicians demonstrated greater knowledge of antibiotics and concern about side effects than patients, the predominant categorical gist for both patients and clinicians was "why not take a risk," which compares the status quo of remaining sick to the possibility of benefit from antibiotics. This gist also predicted expectations and prior prescribing in the nonpatient sample. Other representations reflected the gist that "germs are germs" conflating bacteria and viruses, as well as perceptions of side effects and efficacy. Although individually rational, reliance on the "why not take a risk" representation can lead to socially suboptimal results, including antibiotic resistance and individual patient harm due to adverse events. Changing this representation could alter clinicians' and patients' expectations, suggesting opportunities to reduce overprescribing.

BackgroundAdherence to evidence-based antibiotic therapy guidelines for treatment of upper respiratory tract infections (URIs) varies widely among clinicians. Understanding this variability is key for reducing inappropriate prescribing.ObjectiveTo measure how emergency department (ED) clinicians' perceptions of antibiotic prescribing risks affect their decision-making.DesignClinician survey based on fuzzy-trace theory, a theory of medical decision-making, combined with retrospective data on prescribing outcomes for URI/pneumonia visits in two EDs. The survey predicts the categorical meanings, or gists, that individuals derive from given information.ParticipantsED physicians, residents, and physician assistants (PAs) who completed surveys and treated patients with URI/pneumonia diagnoses between August 2014 and December 2015.Main measuresGists derived from survey responses and their association with rates of antibiotic prescribing per visit.Key resultsOf 4474 URI/pneumonia visits, 2874 (64.2%) had an antibiotic prescription. However, prescribing rates varied from 7% to 91% for the 69 clinicians surveyed (65.2% response rate). Clinicians who framed therapy-prescribing decisions as a categorical choice between continued illness and possibly beneficial treatment ("why not take a risk?" gist, which assumes antibiotic therapy is essentially harmless) had higher rates of prescribing (OR 1.28 [95% CI, 1.06-1.54]). Greater agreement with the "antibiotics may be harmful" gist was associated with lower prescribing rates (OR 0.81 [95% CI, 0.67-0.98]).ConclusionsOur results indicate that clinicians who perceive prescribing as a categorical choice between patients remaining ill or possibly improving from therapy are more likely to prescribe antibiotics. However, this strategy assumes that antibiotics are essentially harmless. Clinicians who framed decision-making as a choice between potential harms from therapy and continued patient illness (e.g., increased appreciation of potential harms) had lower prescribing rates. These results suggest that interventions to reduce inappropriate prescribing should emphasize the non-negligible possibility of serious side effects.

AimCoinfecting bacterial pathogens are a major cause of morbidity and mortality in influenza. However, there remains a paucity of literature on the magnitude of coinfection in influenza patients.MethodA systematic search of MeSH, Cochrane Library, Web of Science, SCOPUS, EMBASE, and PubMed was performed. Studies of humans in which all individuals had laboratory confirmed influenza, and all individuals were tested for an array of common bacterial species, met inclusion criteria.ResultsTwenty-seven studies including 3215 participants met all inclusion criteria. Common etiologies were defined from a subset of eight articles. There was high heterogeneity in the results (I(2)  = 95%), with reported coinfection rates ranging from 2% to 65%. Although only a subset of papers were responsible for observed heterogeneity, subanalyses and meta-regression analysis found no study characteristic that was significantly associated with coinfection. The most common coinfecting species were Streptococcus pneumoniae and Staphylococcus aureus, which accounted for 35% (95% CI, 14%-56%) and 28% (95% CI, 16%-40%) of infections, respectively; a wide range of other pathogens caused the remaining infections. An assessment of bias suggested that lack of small-study publications may have biased the results.ConclusionsThe frequency of coinfection in the published studies included in this review suggests that although providers should consider possible bacterial coinfection in all patients hospitalized with influenza, they should not assume all patients are coinfected and be sure to properly treat underlying viral processes. Further, high heterogeneity suggests additional large-scale studies are needed to better understand the etiology of influenza bacterial coinfection.

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution.

ObjectivesTo explore current practices and decision making regarding antimicrobial prescribing among emergency department (ED) clinical providers.MethodsWe conducted a survey of ED providers recruited from 8 sites in 3 cities. Using purposeful sampling, we then recruited 21 providers for in-depth interviews. Additionally, we observed 10 patient-provider interactions at one of the ED sites. SAS 9.3 was used for descriptive and predictive statistics. Interviews were audio recorded, transcribed, and analyzed using a thematic, constructivist approach with consensus coding using NVivo 10.0. Field and interview notes collected during the observational study were aligned with themes identified through individual interviews.ResultsOf 150 survey respondents, 76% agreed or strongly agreed that antibiotics are overused in the ED, while half believed they personally did not overprescribe. Eighty-nine percent used a smartphone or tablet in the ED for antibiotic prescribing decisions. Several significant differences were found between attending and resident physicians. Interview analysis identified 42 codes aggregated into the following themes: (1) resource and environmental factors that affect care; (2) access to and quality of care received outside of the ED consult; (3) patient-provider relationships; (4) clinical inertia; and (5) local knowledge generation. The observational study revealed limited patient understanding of antibiotic use. Providers relied heavily upon diagnostics and provided limited education to patients. Most patients denied a priori expectations of being prescribed antibiotics.ConclusionsPatient, provider, and healthcare system factors should be considered when designing interventions to improve antimicrobial stewardship in the ED setting.

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. A systematic review of 388 models published between 1970 and 2010 found that the vast majority adopted the Ross-Macdonald assumption of homogeneous transmission in a well-mixed population. Studies comparing models and data question these assumptions and point to the capacity to model heterogeneous, focal transmission as the most important but relatively unexplored component in current theory. Fine-scale heterogeneity causes transmission dynamics to be nonlinear, and poses problems for modeling, epidemiology and measurement. Novel mathematical approaches show how heterogeneity arises from the biology and the landscape on which the processes of mosquito biting and pathogen transmission unfold. Emerging theory focuses attention on the ecological and social context for mosquito blood feeding, the movement of both hosts and mosquitoes, and the relevant spatial scales for measuring transmission and for modeling dynamics and control.

Coagulase-negative staphylococci (CoNS) are important bloodstream pathogens that are typically resistant to multiple antibiotics. Despite the concern about increasing resistance, there have been no recent studies describing the national prevalence of CoNS pathogens. We used national resistance data over a period of 13 years (1999 to 2012) from The Surveillance Network (TSN) to determine the prevalence of and assess the trends in resistance for Staphylococcus epidermidis, the most common CoNS pathogen, and all other CoNS pathogens. Over the course of the study period, S. epidermidis resistance to ciprofloxacin and clindamycin increased steadily from 58.3% to 68.4% and from 43.4% to 48.5%, respectively. Resistance to levofloxacin increased rapidly from 57.1% in 1999 to a high of 78.6% in 2005, followed by a decrease to 68.1% in 2012. Multidrug resistance for CoNS followed a similar pattern, and this rise and small decline in resistance were found to be strongly correlated with levofloxacin prescribing patterns. The resistance patterns were similar for the aggregate of CoNS pathogens. The results from our study demonstrate that the antibiotic resistance in CoNS pathogens has increased significantly over the past 13 years. These results are important, as CoNS can serve as sentinels for monitoring resistance, and they play a role as reservoirs of resistance genes that can be transmitted to other pathogens. The link between the levofloxacin prescription rate and resistance levels suggests a critical role for reducing the inappropriate use of fluoroquinolones and other broad-spectrum antibiotics in health care settings and in the community to help curb the reservoir of resistance in these colonizing pathogens.

Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross-Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross-Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross-Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.