Background: The Uganda Ministry of Health (MoH) and implementing partners instituted an infection prevention and control (IPC) response strategy during the Uganda SVD outbreak in 2022 that involved rapid enhancement of screening capacity at HCFs. Rapid scale-up of screening for infectious diseases, such as SVD, is critical for early identification and triage of suspected or confirmed cases in HCFs. We describe the rapid deployment of a multimodal IPC strategy implemented in response to the SVD outbreak and the resulting impact on screening measures at HCFs. Methods: We implemented a multimodal IPC strategy in HCFs from five high risk districts to improve screening practices from November 2022–January 2023. The strategy included training health workers (HCWs) identified as IPC mentors; establishing screening areas; and providing screening supplies and communication materials. The three-day training utilized an MoH standardized training package with didactic and practice sessions. The mentors then cascaded screening information and skills to other HCWs through onsite trainings and mentorships and established screening areas. Baseline and endline (3 months after baseline) cross-sectional assessments were conducted using the MoH IPC Assessment Tool adapted from the WHO Ebola IPC Scorecard. The five main screening parameters assessed included presence of ≥ 1 meter distance between screener and the person screened, availability of a functional handwashing facility and infrared thermometer, correct record of each person’s temperature, and appropriate referral process for those suspected of having SVD to holding areas. IPC capacity was measured through the summation of each of these parameter results and calculated as an overall percentage. IBM SPSS Statistics 20 software was used for data analysis and a paired t-test done to determine any significant findings between mean scores (percentage) at baseline and endpoint. Results: A total of 296 IPC mentors were trained, screening information was cascaded to 3,899 HCWs, and screening areas were established in 1,135 HCFs. Based on the screening results from the MoH IPC assessment tool, capacity improved from 44% (SD=37) at baseline to 67% (SD=34) at endpoint. Screening capacity improved from baseline to endpoint among level II and public HCFs from 33% (SD=35) to 60% (SD=35) (p < 0 .05) and from 54% (SD=38) to 76% (SD=31) (p < 0 .05), respectively. Conclusion: Rapid implementation of a multimodal IPC strategy was successful in enhancing screening capacity across Uganda’s HCFs during a SVD response, which is critical for early identification of infected patients to interrupt transmission. This multimodal approach should be recommended for future response actions.

Many people who experience opioid use disorder rely on Medicaid. The high penetration of managed care systems into Medicaid raises the importance of understanding states’ expectations regarding coverage, access to care, and health system performance and effectively elevates agreements between states and plans into blueprints for coverage and care. Federal law broadly regulates these structured agreements while leaving a high degree of discretion to states and plans. In this study, researchers reviewed the provisions of 15 state Medicaid managed care contract related to substance use disorder (SUD) treatment to identify whether certain elements of SUD treatment were a stated expectation and the extent to which the details of those expectations varied across states in ways that ultimately could affect evaluation of performance and health outcomes. We found that while all states include SUD treatment as a stated contract expectation, discussions around coverage of specific services and nationally recognized guidelines varied. These variations reflect key state choices regarding how much deference to afford their plans in coverage design and plan administration and reveal important differences in purchasing expectations that could carry implications for efforts to examine similarities and differences in access, quality, and health outcomes within managed care across the states.

A closed-form solution is presented for internal compressible flows with an arbitrary combination of area change, heat addition, friction and non-uniform flow. The approach assumes steady flow of a calorically perfect ideal gas using the integral form of the governing equations. It is shown that the equations reduce to a simple, bi-quadratic equation in exit Mach number. This solution is compared to previously known solutions for Rayleigh flow, Fanno flow, isentropic flow and normal shock relations. Theoretical solutions for sudden expansion, sudden contraction, and supersonic–supersonic two-stream constant area mixing are also presented. These solutions were found to be in agreement with data available in the literature.

Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.

For an increasing proportion of Australian households, the Australian dream of home ownership is no longer an option. Neoliberal housing policy and the financialisation of housing has resulted in a housing affordability crisis. Historically, Australian housing policy has afforded only a limited role to local government. This article analyses the results of a nation-wide survey of Australian local governments’ perceptions of housing affordability in their local government area, the possibilities for their meaningful intervention, the challenges they face, the role of councillors and councils’ perceptions of what levels of government should take responsibility for housing. Almost all of the respondents from Sydney and Melbourne councils were clear that there is a housing affordability crisis in their local government area. We apply a framework analysing housing policy in the context of neoliberalism and the related financialisation of housing in order to analyse the housing affordability crisis in Sydney and Melbourne. We conclude that in order to begin resolving the housing crisis in Australia’s two largest cities there has to be an increasing role for local government, a substantial increase in the building of social and affordable housing and a rollback of policies that encourage residential property speculation.

There are many pressures on elite footballers. They work in a meritocracy, where only the best are selected and play at the highest levels. From the moment they enter an academy to their retirement there is a fear of deselection and rejection. Elite players need to contend with criticisms from fans and via social media; team and management dynamics can be stressful. Fears of injury are major concerns. In addition, the players are likely to face everyday difficulties experienced by the rest of society, such as relationship, family and financial problems. There is a great deal of stigma associated with mental health problems in footballers, hence approaches are required that are destigmatising. This article presents two frameworks conceptualising stress in footballers: the Power Threat Meaning Framework, which describes stress in non-diagnostic terms; and the Yerkes–Dodson curve, which describes how stress can affect footballers’ mental and physical performances on the pitch. Both frameworks can combine to enable therapists to understand players’ distress and its impact and to guide towards appropriate treatments, as we show in a fictitious case study.

Substantial progress has been made in the standardization of nomenclature for paediatric and congenital cardiac care. In 1936, Maude Abbott published her Atlas of Congenital Cardiac Disease, which was the first formal attempt to classify congenital heart disease. The International Paediatric and Congenital Cardiac Code (IPCCC) is now utilized worldwide and has most recently become the paediatric and congenital cardiac component of the Eleventh Revision of the International Classification of Diseases (ICD-11). The most recent publication of the IPCCC was in 2017. This manuscript provides an updated 2021 version of the IPCCC.

The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD), in collaboration with the World Health Organization (WHO), developed the paediatric and congenital cardiac nomenclature that is now within the eleventh version of the International Classification of Diseases (ICD-11). This unification of IPCCC and ICD-11 is the IPCCC ICD-11 Nomenclature and is the first time that the clinical nomenclature for paediatric and congenital cardiac care and the administrative nomenclature for paediatric and congenital cardiac care are harmonized. The resultant congenital cardiac component of ICD-11 was increased from 29 congenital cardiac codes in ICD-9 and 73 congenital cardiac codes in ICD-10 to 318 codes submitted by ISNPCHD through 2018 for incorporation into ICD-11. After these 318 terms were incorporated into ICD-11 in 2018, the WHO ICD-11 team added an additional 49 terms, some of which are acceptable legacy terms from ICD-10, while others provide greater granularity than the ISNPCHD thought was originally acceptable. Thus, the total number of paediatric and congenital cardiac terms in ICD-11 is 367. In this manuscript, we describe and review the terminology, hierarchy, and definitions of the IPCCC ICD-11 Nomenclature. This article, therefore, presents a global system of nomenclature for paediatric and congenital cardiac care that unifies clinical and administrative nomenclature.

The members of ISNPCHD realize that the nomenclature published in this manuscript will continue to evolve. The version of the IPCCC that was published in 2017 has evolved and changed, and it is now replaced by this 2021 version. In the future, ISNPCHD will again publish updated versions of IPCCC, as IPCCC continues to evolve.

On January 29, 2020, a total of 195 US citizens were evacuated from the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China, to March Air Reserve Base in Riverside, California, and entered the first federally mandated quarantine in over 50 years. With less than 1-d notice, a multi-disciplinary team from Riverside County and Riverside University Health System in conjunction with local and federal agencies established on-site 24-h medical care and behavioral health support. This report details the coordinated efforts by multiple teams that took place to provide care for the passengers and to support the surrounding community.

Antibiotics are among the most common medications prescribed in nursing homes. The annual prevalence of antibiotic use in residents of nursing homes ranges from 47% to 79%, and more than half of antibiotic courses initiated in nursing-home settings are unnecessary or prescribed inappropriately (wrong drug, dose, or duration). Inappropriate antibiotic use is associated with a variety of negative consequences including Clostridioides difficile infection (CDI), adverse drug effects, drug–drug interactions, and antimicrobial resistance. In response to this problem, public health authorities have called for efforts to improve the quality of antibiotic prescribing in nursing homes.

Background:Clostridioides difficile infection (CDI) following colorectal surgery can lead to significant adverse outcomes. Although previous studies have identified risk factors for CDI, their relative importance for predicting complications remains unclear. Objective: We sought to use machine-learning algorithms to accurately determine which perioperative risk factors are most predictive of adverse outcomes after CDI. Methods: The National Surgical Quality Improvement Project (NSQIP) database was used to identify all patients who developed CDI after a colorectal operation in 2016 (N = 14,392). We excluded patients without CDI and patients <18 years of age. Any missing data were replaced with multivariate singular value decomposition imputation. We collected data on patient demographics, comorbidities, preoperative laboratory values, operative details, and outcomes, including infectious, cardiovascular, hematologic, renal, and pulmonary complications, unplanned returns to the operating room (RTOR), non–home discharge, readmission, and mortality. Data were univariably assessed for significant association with outcomes. If an input variable significantly correlated with ≥5 outcomes, it was included in our machine-learning models. We utilized bootstrap aggregation with random forests to improve prediction accuracy. We then calculated each input variable’s importance to the model outcome (VIP). The VIPs of each variable were averaged to yield an overall impact. Each model’s accuracy was determined by the area under the receiver operator curve (AUROC). Results: There were 841 patients in our cohort (median age 66 years (IQR, 55–75.8), 482 (57%) were women, and the mean American Society of Anesthesiologists [ASA] class score was 2.9 (SD, ±0.7). Of all colorectal surgeries, 172 (20.5%) were emergent. Overall mortality was 3.8% (n=32), and 371 patients (44.1%) experienced at least 1 postoperative complication, of which infectious complications (eg, septic shock, sepsis, wound infection, urinary tract infection) were most common (n=255, 30.3%). The RTOR rate was 10.3% (n = 87), the non–home discharge rate was 23.8% (n = 200), and the readmission rate was 30.9% (n = 260). The input variables most predictive of any adverse outcome were hematocrit (VIP, 24.9%), ASA class (VIP, 24.4%), creatinine (VIP, 17.4%), and prealbumin (VIP, 11.6%). The probability of any adverse outcome was 90.6% in the setting of hematocrit ≤27%, ASA class ≥3, creatinine ≥1.6 mg/dL, and prealbumin ≤3.1 mg/dL. All machine-learning models had an AUROC ≥0.99. Conclusions: Although nonpatient factors can contribute to unfavorable outcomes in patients with CDI following colorectal surgery, we identified 4 patient-specific variables that account for almost 80% of any adverse outcomes. Although further prospective study is needed, individuals with these preoperative risk factors could consider delaying their elective colorectal operations until they are medically optimized.

Funding: None

Disclosures: None