Sur le nombre réel d'infections au COVID-19: effet de la sensibilité, de la spécificité et du nombre de tests sur la Estimation de la Prévalence

In this report, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio between the number of positive tests and the total number of tested individuals leads to dramatically high estimation errors, and thus, unadapted public health policies. The relevance of estimating the prevalence ratio using the formula presented in this work is that precision increases with the number of tests. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved with a finite number of tests, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different from one. Second, the key parameter for reducing the estimation error is the number of tests. For large number of tests, as long as the sum of the sensitivity and specificity is different from one, the exact values of these parameters have very little impact on the estimation error.Ce rapport présente une formule mathématique pour estimer le nombre d’infections SARS-CoV-2 dans une population donnée. La formule utilise les résultats et les paramètres des tests, c’est-à-dire la probabilité de vrais positifs (sensibilité) et de vrais négatifs (spécificité). Selon la sensibilité et la spécificité des tests, le nombre de résultats positifs peut être radicalement différent du nombre d’individus infectés. Ainsi, le nombre final de résultats rendus positifs n’est pas une source d’information fiable pour la prise de décision ou l’élaboration des directives.Deux conclusions sont tirées de ce travail; afin de garantir l’obtention d’une estimation fiable,des campagnes de tests doivent être mises en oeuvre avec des tests pour lesquels la somme de la sensibilité et de la spécificité est significativement différente de un. De plus, il est prouvé qu’un nombre important de tests conduit à une estimation plus précise du nombre d’infectés. Pour un grand nombre de tests, tant que la somme de la sensibilité et de la spécificité n’est pas égale à un, les valeurs exactes de ces paramètres ont très peu d’impact sur l’erreur d’estimation

On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation

In this paper, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio between the number of positive tests and the total number of tested individuals leads to dramatically high estimation errors, and thus, unadapted public health policies. The relevance of estimating the prevalence ratio using the formula presented in this work is that precision increases with the number of tests. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved with a finite number of tests, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different than one. Second, the key parameter for reducing the estimation error is the number of tests. For a large number of tests, as long as the sum of the sensitivity and specificity is different than one, the exact values of these parameters have very little impact on the estimation error

Mindfulness as a Protective Factor Against Increased Tobacco and Alcohol Use in Hospital Workers Following the First COVID-19-Related Lockdown: a Study in Southern France

International audienceCOVID-19-related national lockdowns worldwide have had repercussions on people’s well-being and have led to increased substance use. Mindfulness has previously been associated with reduced psychological distress and benefits in terms of addictive behaviors. We aimed to assess whether dispositional mindfulness protected against increased tobacco and alcohol use in hospital workers after France’s first lockdown started. All workers in two French hospitals were contacted by email to participate in an online survey. Three hundred eighty-five workers answered. We ran two separate logistic regression models to test for associations between the level of dispositional mindfulness and both increased tobacco and alcohol use, after adjusting for affect deterioration. Dispositional mindfulness was associated with a lower likelihood of increased tobacco (adjusted odds ratio (AOR) [95% CI] 0.71 [0.51; 0.99], p = 0.046) and alcohol (0.66 [0.50; 0.87], p = 0.004) use. The effect of mindfulness on tobacco use was partially mediated by affect deterioration. Dispositional mindfulness appeared to be a protective factor against lockdown-related tobacco and alcohol use increases in French hospital workers

Examining the Relationships between Mindfulness and Tobacco Craving Factors

International audienceBackground: Relationships between mindfulness and general craving have been documented. However, there is still no data regarding relationships between mindfulness and the different craving factors.Methods: Using data from an online survey among hospital workers smoking tobacco in France (n = 127), we performed linear regression models with the four craving factors as outcomes, and dispositional mindfulness as explanatory variable.Results: After adjusting for nicotine dependence, mindfulness was negatively associated with general craving and three out of four craving factors (emotionality, compulsivity and purposefulness, but not expectancy).Conclusions: These results support the implementation of mindfulness-based interventions in the context of tobacco cessation attempt

Psychological Distress and Tobacco Use Among Hospital Workers During COVID-19

International audienc