Transmission dynamics of Q fever in French Guiana: A population-based cross-sectional study.

Background: Q fever is a zoonosis caused by Coxiella burnetii which is among the major agents of community-acquired pneumonia in French Guiana. Despite its relatively high incidence, its epidemiology in French Guiana remains unclear, and all previous studies have considered transmission from livestock unlikely, suggesting that a wild reservoir is responsible for transmission. Methods: A country-wide seroprevalence survey of 2697 participants from French Guiana was conducted. Serum samples were tested for phase II IgG antibodies by ELISA and indirect immunofluorescence assays (IFAs). Factors associated with Q fever were investigated, and a serocatalytic model was used to reconstruct the annual force of infection. Findings: The overall weighted seroprevalence was estimated at 9.6% (95% confidence interval (CI): 8.2%-11.0%). The model revealed constant, low-level circulation across French Guiana, particularly affecting middle-aged males (odds ratio (OR): 3.0, 95% credible interval (CrI): 1.7-5.8) and individuals living close to sheep farms (OR: 4, 95% CrI: 1.5-12). The overall annual number of cases was estimated at 579 (95% CrI: 492-670). In the region around Cayenne, the main urban municipality, the high seroprevalence was explained by an outbreak that may have occurred between 1996 and 2003 and that infected 10% (95% CrI: 6.9%-14%) of the population and males and females alike. Interpretation: This study reveals for the first time Q fever dynamics of transmission and the role of domestic livestock in transmission in French Guiana and highlights the urgent need to reinforce Q fever surveillance in livestocks of the entire Guianese territory. Funding: This study was supported by the "European Regional Development Fund" under EPI-ARBO grant agreement (GY0008695), the "Regional Health Agency of French Guiana" and the "National Center of Spatial Studies". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Superspreading events of SARS-CoV-2 in Paris: A retrospective analysis of data from the first wave of COVID-19 in 2020.

BACKGROUND: The 2020 COVID-19 pandemic led to a strict lockdown in France from March 17 to May 11, 2020. After the lockdown, the French strategy to mitigate the impact of SARS-CoV-2 relied partly on investigations of all confirmed cases. Monitoring collective settings is particularly important since SARS-CoV-2 seems prone to superspreading events (SSEs). METHODS: Our study is based on data gathered in Paris from May 11 to December 31, 2020, by the Ile-de-France Regional Health Agency (RHA) to investigate cases occurring in collective and high-risk settings. Specific events in high-risk settings were systematically transmitted to the RHA, and screenings were organized by the facilities, while other settings were reported when three cases were identified within a short period. These settings were more difficult to identify through the surveillance system since no systematic screening was organized by the facility, leaving screenings to rely on the national contact-tracing programme. No official superspreading threshold has been set for SARS-CoV-2. We defined a SSE as an event involving ten cases. RESULTS: We analysed 15,706 events associated with 38,670 cases, representing an average of 2.70 cases per event. Most clusters occurred in educational facilities, workplace environments, social care settings, and healthcare facilities. SSEs represented 3.4% but accounted for 28% of all cases reported. The highest number of SSEs occurred in college settings (12.6%), followed by hospitals and retirement homes. Educational facilities had the lowest number of SSEs, with around 1% in preschools and elementary schools. CONCLUSIONS: We observed different SSE rates in each setting. Preschools and primary schools represented the majority of events but experiencing very few SSEs. Colleges were prone to SSEs and were associated with a high number of secondary cases. These findings provide some insights on contact tracing activities and SARS-CoV-2 transmission in different settings.

Screening four broad categories of eating disorders: suitability of a clinical algorithm adapted from the SCOFF questionnaire.

BACKGROUND: We evaluated the performance of a clinical algorithm (Expali™), combining two or more positive answers to SCOFF questionnaire with Body Mass Index (BMI), to identify four Broad Categories of eating disorders (ED) derived from DSM-5. METHODS: The clinical algorithm (Expali™) was developed from 104 combinations of BMI levels and answers to five SCOFF questions with at least two positive answers. Two senior ED physicians allocated each combination to one of the four Broad Categories of ED derived from DSM-5: restrictive disorder, bulimic disorder, hyperphagic disorder and other unspecified ED diagnosed by ED clinicians. The performance of Expali™ was evaluated on data from 206 patients with ED. Sensitivity, specificity values and Youden index were calculated for each category. RESULTS: The 206 patients were diagnosed as follows: 31.5% restrictive disorder, 18.9% bulimic disorder, 40.8% hyperphagic disorder and 8.8% other ED. The sensitivity of Expali™ for restrictive, bulimic, hyperphagic and other unspecified ED were respectively: 76.9, 69.2, 79.7 and 16.7%. The Youden index was respectively 0.73, 0.57, 0.67 and 0.07. CONCLUSIONS: In a SCOFF-positive ED population (at least two positive answers), the clinical algorithm Expali™ demonstrated good suitability by correctly classifying three of the four Broad Categories of eating disorders (restrictive, bulimic and hyperphagic disorder). It could be useful both to healthcare professionals and the general population to enable earlier detection and treatment of ED and to improve patient outcomes.