ABSTRACT
BACKGROUND: Allocation of scarce medical resources can be based on different principles. It has not yet been investigated which allocation schemes are preferred by medical laypeople in a particular situation of medical scarcity like an emerging infectious disease and how the choices are affected by providing information about expected population-level effects of the allocation scheme based on modelling studies. We investigated the potential benefit of strategic communication of infectious disease modelling results. METHODS: In a two-way factorial experiment (n = 878 participants), we investigated if prognosis of the disease or information about expected effects on mortality at population-level (based on dynamic infectious disease modelling studies) influenced the choice of preferred allocation schemes for prevention and treatment of an unspecified sexually transmitted infection. A qualitative analysis of the reasons for choosing specific allocation schemes supplements our results. RESULTS: Presence of the factor "information about the population-level effects of the allocation scheme" substantially increased the probability of choosing a resource allocation system that minimized overall harm among the population, while prognosis did not affect allocation choices. The main reasons for choosing an allocation scheme differed among schemes, but did not differ among those who received additional model-based information on expected population-level effects and those who did not. CONCLUSIONS: Providing information on the expected population-level effects from dynamic infectious disease modelling studies resulted in a substantially different choice of allocation schemes. This finding supports the importance of incorporating model-based information in decision-making processes and communication strategies.
Subject(s)
Communicable Diseases , Resource Allocation , HumansABSTRACT
BACKGROUND: Comprehensive evidence synthesis on the associations between comorbidities and behavioural factors with hospitalisation, intensive care unit (ICU) admission, and death due to COVID-19 is required for deriving national and international recommendations on primary targets for non-pharmacological interventions (NPI) and vaccination strategies. METHODS: We performed a rapid systematic review and meta-analysis on studies and publicly accessible data to quantify associations between predisposing health conditions, demographics, behavioural factors on the one hand and hospitalisation, ICU admission, and death from COVID-19 on the other hand. We provide ranges of reported and calculated effect estimates and pooled relative risks derived from a meta-analysis and meta-regression. RESULTS: Seventy-five studies were included in qualitative and 74 in quantitative synthesis, with study populations ranging from 19 to 44,672 COVID-19 cases. The risk of dying from COVID-19 was significantly associated with cerebrovascular [pooled relative risk (RR) 2.7 (95% CI 1.7-4.1)] and cardiovascular [RR 3.2 (CI 2.3-4.5)] diseases, hypertension [RR 2.6 (CI 2.0-3.4)], and renal disease [RR 2.5 (CI 1.8-3.4)], with high heterogeneity in pooled estimates, partly but not solely explained by age of study participants. For some comorbidities, our meta-regression showed a decrease in effect on the severity of disease with a higher median age of the study population. Compared to death, associations between several comorbidities and hospitalisation and ICU admission were less pronounced. CONCLUSIONS: We obtained robust estimates on the magnitude of risk for COVID-19 hospitalisation, ICU admission, and death associated with comorbidities, demographic, and behavioural risk factors and show that these estimates are modified by age of study participants. This interaction is an important finding to be kept in mind for current vaccination strategies and for the protection of individuals with high risk for a severe COVID-19 course.