Background: The relationship between lag time and outcomes in retinoblastoma (RB) is unclear. In this study, we aimed to study the effect of lag time between onset of symptoms and diagnosis of retinoblastoma (RB) in countries based on their national-income and analyse its effect on the outcomes. Methods: We performed a prospective study of 692 patients from 11 RB centres in 10 countries from 1 January 2019 to 31 December 2019. Results: The following factors were significantly different among different countries based on national-income level: age at diagnosis of RB (p = 0.001), distance from home to nearest primary healthcare centre (p = 0.03) and mean lag time between detection of first symptom to visit to RB treatment centre (p = 0.0007). After adjusting for country income, increased lag time between onset of symptoms and diagnosis of RB was associated with higher chances of an advanced tumour at presentation (p < 0.001), higher chances of high-risk histopathology features (p = 0.003), regional lymph node metastasis (p < 0.001), systemic metastasis (p < 0.001) and death (p < 0.001). Conclusions: There is a significant difference in the lag time between onset of signs and symptoms and referral to an RB treatment centre among countries based on national income resulting in significant differences in the presenting features and clinical outcomes.

The COVID-19 pandemic cast a dramatic spotlight on the use of data as a fundamental component of good decision-making. Evaluating and comparing alternative policies required information on concurrent infection rates and insightful analysis to project them into the future. Statisticians in Israel were involved in these processes early in the pandemic in some silos as an ad-hoc unorganized effort. Informal discussions within the statistical community culminated in a roundtable, organized by three past presidents of the Israel Statistical Association, and hosted by the Samuel Neaman Institute in April 2021. The meeting was designed to provide a forum for exchange of views on the profession’s role during the COVID-19 pandemic, and more generally, on its influence in promoting evidence-based public policy. This paper builds on the insights and discussions that emerged during the roundtable meeting and presents a general framework, with recommendations, for involving statisticians and statistics in decision-making.

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Mathematical and statistical models have played an important role in the analysis of data from COVID-19. They are important for tracking the progress of the pandemic, for understanding its spread in the population, and perhaps most significantly for forecasting the future course of the pandemic and evaluating potential policy options. This article describes the types of models that were used by research teams in Israel, presents their assumptions and basic elements, and illustrates how they were used, and how they influenced decisions. The article grew out of a “modelists’ dialog” organized by the Israel National Institute for Health Policy Research with participation from some of the leaders in the local modeling effort.