Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach.

BACKGROUND: Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination. METHODS: We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons. RESULTS: Based on 2 case definitions, we estimate between 0.42-3.2% and 0.33-1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08-0.61% and 0.07-0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32-2.4 million in 2011-2012 and 1.8-8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4-34 million in 2011-2012 and 23-102 million in 2012-2013. CONCLUSIONS: We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

Teaching a lay theory before college narrows achievement gaps at scale.

Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.