This study examines the impact of personalized gender-based communication to encourage the screening of depression and seeking out mental health care consultation. An internet search engine advertisement was deployed on Bing, Microsoft during the COVID-19 pandemic lockdowns in the Provence–Alpes–Côte d'Azur (PACA) region in France during the month of May 2020, the height of the France lockdowns. A two-armed study was conducted with Arm A containing a non-personalized (control) advertisement and Arm B containing a personalized gender-based advertisement. 53,185 advertisements were shown between the two arms. Results show that receiving a personalized gender-based message increases the probability of clicking on the advertisement. However, upon clicking the advertisement, there was no significant difference in the completion of the depression questionnaire between the two groups. These results suggest that although personalized gender messaging is effective at drawing in a greater click rate, it did not increase, nor decreased, the conversion rate to monitor depression by self-assessment.

Cette étude a pour objectif d’évaluer différents modes de financement de la couverture santé universelle au Sénégal. La méthode utilisée, la micro-simulation, permet d’examiner l’impact de différents scenarii sur les consommations des ménages ainsi que sur les dépenses publiques. Les résultats montrent que la généralisation d’une assurance-maladie à l’ensemble de la population, associée à une réduction des coûts directs des soins, augmenterait les consommations de soins des Sénégalais, améliorant donc leur accès aux services de santé. Néanmoins, une telle généralisation serait coûteuse pour les finances publiques. Pour limiter les coûts supportés par le gouvernement, l’augmentation du taux d’imposition sur la consommation et de la prime de contribution à l’assurance-maladie serait utile et permettrait de ramener les finances publiques à l’équilibre.

We study physicians’ incentives to use personalised medicine techniques, replicating the physician’s trade-offs under the option of personalised medicine information. In a laboratory experiment conducted in two French Universities, prospective physicians played a real-effort game. We vary both the information structure (free access versus paid access to personalised medicine information) and the payment scheme (pay-for-performance (P4P), capitation (CAP) and fee-for-service (FFS)), implementing a within-subject design. Our results are threefold: (i) Compared to FFS and CAP, the P4P scheme strongly and positively impacts the decision to adopt personalised medicine. (ii) Although expected to dominate the other schemes, P4P is not always efficient in transforming free access to personalised medicine into higher quality of care. (iii) When it has to be paid for and after controlling for self-selection, personalised medicine is positively associated with quality, suggesting that subjects tend to make better use of information that comes at a cost. We find this effect to be stronger for males than for females prospective physicians. Quantification of our results however suggests that this positive impact is not strong enough to justify generalising the payment for personalised medicine access. Finally, we develop a theoretical model that includes in its set-up a commitment device component, which is the mechanism that we inferred from the data of the experiment. Our model replicates the principal results of the experiment, reinforcing the interpretation that the higher quality provided by subjects who bought personalised medicine can be interpreted as a commitment device effect.

Online surveys of health professionals have become increasingly popular during the COVID-19 crisis because of their ease, speed of implementation, and low cost. This article leverages an online survey of general practitioners’ (GPs’) attitudes toward the soon-to-be-available COVID-19 vaccines, implemented in October–November 2020 (before the COVID-19 vaccines were authorized in France), to study the evolution of the distribution of their demographic and professional characteristics and opinions about these vaccines, as the survey fieldwork progressed, as reminders were sent out to encourage them to participate. Focusing on the analysis of the potential determinants of COVID-19 vaccine acceptance, we also tested if factors related to survey participation biased the association estimates. Our results show that online surveys of health professionals may be subject to significant selection bias that can have a significant impact on estimates of the prevalence of some of these professionals’ behavioral, opinion, or attitude variables. Our results also highlight the effectiveness of reminder strategies in reaching hard-to-reach professionals and reducing these biases. Finally, they indicate that weighting for nonparticipation remains indispensable and that methods exist for testing (and correcting) selection biases.