Many national organizations call for medical students to receive more public health education in medical school. Nonetheless, limited evidence exists about successful servicelearning programs that administer preventive health services in nonclinical settings. The Flu Crew program, started in 2001 at the Stanford University School of Medicine, provides preclinical medical students with opportunities to administer influenza immunizations in the local community. Medical students consider Flu Crew to be an important part of their medical education that cannot be learned in the classroom. Through delivering vaccines to where people live, eat, work, and pray, Flu Crew teaches medical students about patient care, preventive medicine, and population health needs. Additionally, Flu Crew allows students to work with several partners in the community in order to understand how various stakeholders improve the delivery of population health services. Flu Crew teaches students how to address common vaccination myths and provides insights into implementing public health interventions. This article describes the Stanford Flu Crew curriculum, outlines the planning needed to organize immunization events, shares findings from medical students’ attitudes about population health, highlights the program’s outcomes, and summarizes the lessons learned. This article suggests that Flu Crew is an example of one viable service-learning modality that supports influenza vaccinations in nonclinical settings while simultaneously benefiting future clinicians.
Background Postoperative acute kidney injury (AKI) is a common complication of major gastrointestinal surgery with an impact on short- and long-term survival. No validated system for risk stratification exists for this patient group. This study aimed to validate externally a prognostic model for AKI after major gastrointestinal surgery in two multicentre cohort studies. Methods The Outcomes After Kidney injury in Surgery (OAKS) prognostic model was developed to predict risk of AKI in the 7 days after surgery using six routine datapoints (age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker). Validation was performed within two independent cohorts: a prospective multicentre, international study (‘IMAGINE’) of patients undergoing elective colorectal surgery (2018); and a retrospective regional cohort study (‘Tayside’) in major abdominal surgery (2011–2015). Multivariable logistic regression was used to predict risk of AKI, with multiple imputation used to account for data missing at random. Prognostic accuracy was assessed for patients at high risk (greater than 20 per cent) of postoperative AKI. Results In the validation cohorts, 12.9 per cent of patients (661 of 5106) in IMAGINE and 14.7 per cent (106 of 719 patients) in Tayside developed 7-day postoperative AKI. Using the OAKS model, 558 patients (9.6 per cent) were classified as high risk. Less than 10 per cent of patients classified as low-risk developed AKI in either cohort (negative predictive value greater than 0.9). Upon external validation, the OAKS model retained an area under the receiver operating characteristic (AUC) curve of range 0.655–0.681 (Tayside 95 per cent c.i. 0.596 to 0.714; IMAGINE 95 per cent c.i. 0.659 to 0.703), sensitivity values range 0.323–0.352 (IMAGINE 95 per cent c.i. 0.281 to 0.368; Tayside 95 per cent c.i. 0.253 to 0.461), and specificity range 0.881–0.890 (Tayside 95 per cent c.i. 0.853 to 0.905; IMAGINE 95 per cent c.i. 0.881 to 0.899). Conclusion The OAKS prognostic model can identify patients who are not at high risk of postoperative AKI after gastrointestinal surgery with high specificity. Presented to Association of Surgeons in Training (ASiT) International Conference 2018 (Edinburgh, UK), European Society of Coloproctology (ESCP) International Conference 2018 (Nice, France), SARS (Society of Academic and Research Surgery) 2020 (Virtual, UK).
Pregnant women are a highly vaccine-resistant population and face unique circumstances that complicate vaccine decision-making. Pregnant women are also at increased risk of adverse maternal and neonatal outcomes to many vaccine-preventable diseases. Several models have been proposed to describe factors informing vaccine hesitancy and acceptance. However, none of these existing models are applicable to the complex decision-making involved with vaccine acceptance during pregnancy. We propose a model for vaccine decision-making in pregnancy that incorporates the following key factors: (1) perceived information sufficiency regarding vaccination risks during pregnancy, (2) harm avoidance to protect the fetus, (3) relationship with a healthcare provider, (4) perceived benefits of vaccination, and (5) perceived disease susceptibility and severity during pregnancy. In addition to these factors, the availability of research on vaccine safety during pregnancy, social determinants of health, structural barriers to vaccine access, prior vaccine acceptance, and trust in the healthcare system play roles in decision-making. As a final step, the pregnant individual must balance the risks and benefits of vaccination for themselves and their fetus, which adds greater complexity to the decision. Our model represents a first step in synthesizing factors informing vaccine decision-making by pregnant women, who represent a highly vaccine-resistant population and who are also at high risk for adverse outcomes for many infectious diseases.
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