We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index (Baunez et al., 2021) as a simple measure of viral spread dynamics. Not correcting results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13, 2020—October 26, 2022), our decomposition shows that the reproduction number, when considered alone, characteristically underestimates the resurgence of the pandemic, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would combine the reproduction number with the test and infectivity intensities.
Meta-analyses have shown that preexisting mental disorders may increase serious Coronavirus Disease 2019 (COVID-19) outcomes, especially mortality. However, most studies were conducted during the first months of the pandemic, were inconclusive for several categories of mental disorders, and not fully controlled for potential confounders. Our study objectives were to assess independent associations between various categories of mental disorders and COVID-19-related mortality in a nationwide sample of COVID-19 inpatients discharged over 18 months and the potential role of salvage therapy triage to explain these associations.
Methods and findings:
We analysed a nationwide retrospective cohort of all adult inpatients discharged with symptomatic COVID-19 between February 24, 2020 and August 28, 2021 in mainland France. The primary exposure was preexisting mental disorders assessed from all discharge information recorded over the last 9 years (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, other learning disabilities, and other disorder requiring psychiatric ward admission). The main outcomes were all-cause mortality and access to salvage therapy (intensive-care unit admission or life-saving respiratory support) assessed at 120 days after recorded COVID-19 diagnosis at hospital. Independent associations were analysed in multivariate logistic models. Of 465,750 inpatients with symptomatic COVID-19, 153,870 (33.0%) were recorded with a history of mental disorders. Almost all categories of mental disorders were independently associated with higher mortality risks (except opioid use disorders) and lower salvage therapy rates (except opioid use disorders and Down syndrome). After taking into account the mortality risk predicted at baseline from patient vulnerability (including older age and severe somatic comorbidities), excess mortality risks due to caseload surges in hospitals were +5.0% (95% confidence interval (CI), 4.7 to 5.2) in patients without mental disorders (for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline) and significantly higher in patients with mental disorders (+9.3% [95% CI, 8.9 to 9.8] for a predicted risk of 21.2% [95% CI, 21.0 to 21.4] at baseline). In contrast, salvage therapy rates during caseload surges in hospitals were significantly higher than expected in patients without mental disorders (+4.2% [95% CI, 3.8 to 4.5]) and lower in patients with mental disorders (−4.1% [95% CI, −4.4; −3.7]) for predicted rates similar at baseline (18.8% [95% CI, 18.7-18.9] and 18.0% [95% CI, 17.9-18.2], respectively). The main limitations of our study point to the assessment of COVID-19-related mortality at 120 days and potential coding bias of medical information recorded in hospital claims data, although the main study findings were consistently reproduced in multiple sensitivity analyses.
COVID-19 patients with mental disorders had lower odds of accessing salvage therapy, suggesting that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context.
We study, in a finite setting, the problem of Pareto rationalizability of choice functions by means of a preference profile that is single-peaked with respect to an exogenously given linear order over the alternatives. This problem requires a new condition to be added to those that characterize Pareto rationalizability in the general domain of orders (Moulin (1985)). This new condition appeals to the existence of a central range of options such that the choice function excludes alternatives which are distant from that range.
An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs—May 13 to October 25, 2020—our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59–68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19–28] age group was the lowest and is about half that of the [69–78]. In addition, we propose an algorithm to allocate tests among French “départements” (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.
Comment mesurer le plus finement possible l'accélération ou la décélération d'une épidémie ?
Tests are crucial to know about the number of people who have fallen ill with COVID-19 and to understand in real-time whether the dynamics of the pandemic is accelerating or decelerating. But tests are a scarce resource in many countries. The key but still open question is thus how to allocate tests across sub-national levels. We provide a data-driven and operational criterion to allocate tests efficiently across regions or provinces, with the view to maximize detection of people who have been infected. We apply our criterion to Italian regions and compute the shares of tests that should go to each region, which are shown to differ significantly from the actual distribution.Mickael Degoule
There are two important discussions of commitment in economic literature: one is commitment à la Elster and Schelling, which is related to self-binding choices and means that the person has the desire to restrict the future set of options. The other is commitment à la Sen, which implies a different rationality from the standard maximization rationality and means that the person can choose an option which is not necessarily best for her. In this paper, we set out to show that these two discussions of commitment are related. We do so by presenting a theory of choice under motivation conflict , followed by a discussion of the consequences that the reading of commitment through motivation conflict has on well-being.