Publications
Introduction Alcohol use disorder (AUD) is associated with a significant disease burden in France, where alcohol use is deeply rooted in culture. However, the treatment gap is large because of several barriers, including stigmatisation and drinkers' apprehension about total abstinence. However, standardised and evidence-based interventions based on controlled-drinking for people with AUD are lacking. We aimed to assess the effectiveness of a novel community-based French therapeutic patient education (TPE) program for people with AUD named Choizitaconso. Methods A before-after non-randomised quasi-experimental study, named ETHER, was designed and implemented with people living with AUD, over a period of 6 months. The primary outcome was percentage change in the number of alcohol-related harms experienced. Secondary outcomes were percentage changes in psycho-social patient-reported and community-validated outcomes. Participants in the intervention group (n = 34) benefited from the 10-week TPE program Choizitaconso, while the comparison group (n = 58) received standard care. The Kruskall–Wallis and chi-squared or Fisher's exact tests were used to compare before-after changes in variables in both groups. Linear regression models were used to test for the effect of study group on each outcome and to test for the effect of alcohol consumption as a confounder. Results At 6 months, all outcomes but one either remained stable or numerically improved in both groups. Internalised stigma significantly improved in the intervention group (p = 0.026) but not in the comparison group (p = 0.207), with a significant group effect (p = 0.014). Discussion and Conclusions This study demonstrates the effectiveness of the Choizitaconso TPE program on community-validated outcomes, especially internalised stigma.
Background
Ensuring access to healthcare services is a key element to achieving the Sustainable Development Goal 3 of "promoting healthy lives and well-being for all" through Universal Health Coverage (UHC). However, in the context of low- and middle-income countries, most studies focused on financial protection measured through catastrophic health expenditures (CHE), or on health services utilization among specific populations exhibiting health needs (such as pregnancy or recent sickness).
Methods
This study aims at building an individual score of perceived barriers to medical care (PBMC) in order to predict primary care utilization (or non-utilization). We estimate the score on six items: (1) knowing where to go, (2) getting permission, (3) having money, (4) distance to the facility, (5) finding transport, and (6) not wanting to go alone, using individual data from 1787 adult participants living in rural Senegal. We build the score via a stepwise descendent explanatory factor analysis (EFA), and assess its internal consistency. Finally, we assess the construct validity of the factor-based score by testing its association (univariate regressions) with a wide range of variables on determinants of healthcare-seeking, and evaluate its predictive validity for primary care utilization.
Results
EFA yields a one-dimensional score combining four items with a 0.7 Cronbach's alpha indicating good internal consistency. The score is strongly associated-p-values significant at the 5% level-with determinants of healthcare-seeking (including, but not limited to, sex, education, marital status, poverty, and distance to the health facility). Additionally, the score can predict non-utilization of primary care at the household level, utilization and non-utilization of primary care following an individual's episode of illness, and utilization of primary care during pregnancy and birth. These results are robust to the use of a different dataset.
Conclusion
As a valid, sensitive, and easily documented individual-level indicator, the PBMC score can be a complement to regional or national level health services coverage to measure health services access and predict utilization. At the individual or household level, the PBMC score can also be combined with conventional metrics of financial risk protection such as CHE to comprehensively document deficits in, and progress towards UHC.
While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web-scraping them for the UK on a daily basis, this paper extracts a large database from which we build timely and highly granular indicators. One originality of the dataset is to focus on the supply side of the housing market, allowing to compute innovative indicators reflecting the sellers' perspective such as the number of new listings posted or how prices fluctuate over time for existing listings. Matching listing prices in our dataset with transacted prices from the notarial database, using machine learning, also measures the negotiation margin of buyers. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the “wait-and-see” behaviour of sellers. They also show that listing prices after the lockdown experienced a continued decline in London but increased in other regions.
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.
We present an inexact proximal point algorithm using quasi distances to solve a minimization problem in the Euclidean space. This algorithm is motivated by the proximal methods introduced by Attouch et al., section 4, (Math Program Ser A, 137: 91–129, 2013) and Solodov and Svaiter (Set Valued Anal 7:323–345, 1999). In contrast, in this paper we consider quasi distances, arbitrary (non necessary smooth) objective functions, scalar errors in each objective regularized approximation and vectorial errors on the residual of the regularized critical point, that is, we have an error on the optimality condition of the proximal subproblem at the new point. We obtain, under a coercivity assumption of the objective function, that all accumulation points of the sequence generated by the algorithm are critical points (minimizer points in the convex case) of the minimization problem. As an application we consider a human location problem: How to travel around the world and prepare the trip of a lifetime.
Exloring the role of different types of investors on stock market is crticial since different types of investors react and behave differently when making investment decision. The role of investor behavior is a very important issue in an immature stock market like Vietnam stock market because the market is characterized by a large number of individual investors and low reporting standard. Institutional and foreign investors however play an influential role due to their large exposure and strong investment expertise. Clearly, examining the role of investor behavior and its impact on the stock market in Vietnam is an important topic in finance. A significant body of empirical research has shown that investor behavior is an essential factor to explain stock price that the classical financial theory cannot explain. This research examines the role of investor behavior in stock market by examining the relationship between investor behavior and stock return using the Vietnamese stock exchange data. We create a sentiment index using the principal components analysis (PCA). Consistent with the sentiment and stock return literature, the research shows a negative contemporaneous relationship between investor sentiment and market return.
Background
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.
Conclusions:
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 revisit fertility regulation in Tunisia by examining the role of the extended family. As marriage is the exclusive acknowledged childbearing context, we examine fertility analysis in Tunisia through the sequence: woman’s marriage age, post-marriage delay in the first use of contraception, and past and current contraceptive use. We trace the family socio-economic influences that operate through these decisions.
We prove a new minimization theorem in weighted graphs endowed with a quasi-metric distance, which improves the graphical version of the Ekeland variational principle discovered recently (Alfuraidan and Khamsi in Proc Am Math Soc 147:5313–5321, 2019). As a powerful application in behavioral sciences, we consider how to improve the quality of life in the context of the work–family balance problem, using the recent variational rationality approach of stay and change human dynamics (Soubeyran in Variational Rationality, a Theory of Individual Stability and Change: Worthwhile and Ambidextry Behaviors. Preprint. GREQAM, Aix Marseille University, 2009; Soubeyran in Variational Rationality. The Resolution of Goal Conflicts Via Stop and Go Approach-Avoidance Dynamics. Preprint. AMSE, Aix-Marseille University, 2021).
This article investigates the impact of European Central Bank policies on credits considering financial and banking fragmentation. Using European data from the past decade, we estimate SVAR models to analyze the regional impact of conventional and unconventional measures on price and volume indicators of fragmentation. The risk-taking channel is studied using GVAR models to document the national consequences of this fragmentation. We find that unconventional measures increase credit in peripheral countries. Monetary policies alleviate fragmentation, but mostly in terms of price dispersion rather than credit volume. Finally, unconventional measures imply a rebalancing of European bank assets in favor of foreign currency denominated-assets.
