Background A growing body of research has identified health-related quality-of-life effects for caregivers and family members of ill patients (i.e. 'spillover effects'), yet these are rarely considered in cost-effectiveness analyses (CEAs). Objective The objective of this study was to catalog spillover-related health utilities to facilitate their consideration in CEAs. Methods We systematically reviewed the medical and economic literatures (MEDLINE, EMBASE, and EconLit, from inception through 3 April 2018) to identify articles that reported preference-based measures of spillover effects. We used keywords for utility measures combined with caregivers, family members, and burden. Results Of 3695 articles identified, 80 remained after screening: 8 (10%) reported spillover utility per se, as utility or disutility (i.e. utility loss); 25 (30%) reported a comparison group, either population values (n = 9) or matched, non-caregiver/ family member or unaffected individuals' utilities (n = 16; 3 reported both spillover and a comparison group); and 50 (63%) reported caregiver/family member utilities only. Alzheimer's disease/dementia was the most commonly studied disease/ condition, and the EQ-5D was the most commonly used measurement instrument. Conclusions This comprehensive catalog of utilities showcases the spectrum of diseases and conditions for which caregiver and family members' spillover effects have been measured, and the variation in measurement methods used. In general, utilities indicated a loss in quality of life associated with being a caregiver or family member of an ill relative. Most studies reported caregiver/family member utility without any comparator, limiting the ability to infer spillover effects. Nevertheless, these values provide a starting point for considering spillover effects in the context of CEA, opening the door for more comprehensive analyses. Key PointsInclusion of caregiver and family member ('spillover') quality-adjusted life-years (QALYs) in cost-effectiveness analyses (CEAs) is recommended by multiple national guidance bodies.Caregiver and family member QALYs can include spillover utilities (the independent utility loss due to a family member's illness) that are rarely reported in the literature; more common are caregivers'/family members' utilities, sometimes in combination with a comparator utility.Research gaps remain in spillover effect estimation and incorporation methods, slowing the adoption of these additional measures of burden into cost-effectiveness evaluations.

Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.

In the United States, the field of public health emergency preparedness system research has been supported by the US Centers for Disease Control and Prevention since the release of the 2008 Institute of Medicine letter report. The first definition of public health emergency preparedness appeared in 2007, and before 2008 there was a lack of research and empirical evidence across all 4 research areas identified by the Institute of Medicine. This field can be considered relatively new compared with other research areas in public health; for example, tobacco control research can rely on more than 70 years of knowledge production. However, this review demonstrates that, during the past 7 years, public health emergency preparedness system research has evolved from generic inquiry to the analysis of specific interventions with more empirical studies. Public Health Implications: The results of this review provide an evidence base for public health practitioners responsible for enhancing key components of preparedness and response such as communication, training, and planning efforts.

Objective The worldwide pandemic involving the novel respiratory syndrome (COVID-19) has forced healthcare systems to delay elective operations, including abdominal aortic aneurysm (AAA) repair, to conserve resources. This study provides a structured analysis of the decision to delay AAA repair and quantify the potential for harm. Methods A decision tree was constructed modeling immediate repair of AAA relative to an initial non-operative (delayed repair) approach. Risk of COVID-19 contraction and mortality, aneurysm rupture, and operative mortality were considered. A deterministic sensitivity analysis for a range of patient ages (50 to >80), probability of COVID-19 infection (0.01%-30%), aneurysm size (5.5->7cm), and time horizons (3-9 months) was performed. Probabilistic sensitivity analyses (PSA) were conducted for three representative ages (60, 70, 80). Analyses were conducted for endovascular aortic aneurysm repair (EVAR) and open surgical repair (OSR). Results Patients with aneurysms 7cm or greater demonstrated a higher probability of survival when treated with immediate EVAR or OSR, compared to delayed repair, for patients under 80 years of age. When considering EVAR for aneurysms 5.5-6.9cm, immediate repair had a higher probability of survival except in settings with high probability of COVID-19 infection (10-30%) and advanced age (70-85+ years). A non-operative strategy maximized the probability of survival as patient age or operative risk increased. Probabilistic sensitivity analyses demonstrated that patients with large aneurysms (>7cm) faced a 5.4-7.7% absolute increase in the probability of mortality with a delay of repair of 3 months. Young patients (60-70 years) with 6-6.9cm aneurysms demonstrated an elevated risk of mortality (1.5-1.9%) with a delay of 3 months. Those with 5-5.9cm aneurysms demonstrated an increased survival with immediate repair in young patients (60), however this was small in magnitude (0.2-0.8%). The potential for harm increased as length of surgical delay increased. For elderly patients requiring OSR, in the context of endemic COVID-19, delay of repair improves probability of survival. Conclusion The decision to delay operative repair of AAA should consider both patient age and local COVID-19 prevalence in addition to aneurysm size. EVAR should be considered when possible due to a reduced risk of harm and lower resource utilization.

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