Cross-border epidemiological data integration and harmonization – Application to malaria in the cross-border area between French Guiana and Brazil

International audienceContext Ensure healthy lives and promote well-being for all at all ages Target: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases • Malaria: 216 million cases and 445 thousand deaths worldwide in 2016 (WHO, 2017) • Between 2000 and 2015, 58% and 37% decreases of the mortality and incidence rates, respectively • Socio-demographical and environmental contexts that vulnerabilize local populations • High mobility of pathogen-carrying populations • Patient follow-up difficulties (situations of illegality, auto-medication, ...) • Different strategies and means of surveillance, prevention and control of diseases between countries • Lack of international cooperation: Few regular exchange of comparable data and information between countries ⇒ Heterogeneous data types and formats, nomenclatures and concepts How to ensure cross-border epidemiological data interoperability on an ongoing basis? FTP server Computation and display application (R/Shiny) Harmonized DB Rebuilt DB Rebuilt DB Automated processing chain Integration and harmonization Indicator computation and visualization CHAR/CDPS Cayenne, FG Objectives and challenges • Integrate and harmonize automatically and regularly malaria cross-border epidemiological data • Disseminate indicators and data in an interpretable way for specialists, health actors and population In fine, this should conduct to • Provide a unified vision of the epidemiological cross-border situations • Participate to define concerted, targeted and effective control actions for disease elimination Cross-border malaria considered as a "Major obstacle for malaria elimination" [Wangdi et al., 2015] Paris France Study Region Ethical considerations • Exploitation and dissemination of anonymized data • Temporal and spatial aggregation of the data for public user profile • Authorization of the Commission Nationale de l'Informatique et des Libertés (CNIL) for personal data automatic processing and data transmission to a foreign country (ongoing request No. 2135463) • Multi-platform Extract Transform Load (ETL) tools (TALEND®) Harmonization rules based on expert knowledge in epidemiology and parasitology • Web services for data online publication • Web application (R-Shiny) for online data visualization CDPS ... Funding LMI Sentinela GAPAM Sentinela GCE18-Investment ID OPP1171795 Wangdi K. et al. Cross-border malaria: A major obstacle for malaria elimination. Adv. Parasitol. 2015, 89, 79-107 Barcellos C. et al. An observatory to gather and disseminate information on the health-related effects of environmental and climate change. Pan American Journal of Public Health. 2016. 40(3): 167-73 Roux E. et al. Un "site sentinelle" à la frontière franco-brésilienne pour comprendre et suivre les relations entre climat et santé. In Proceedings of the ENVIBRAS (Environnement et géomatique-approches comparées France Brésil) conference. Rennes; 2014:1-

Disseminated Histoplasmosis in HIV-Infected Patients in South America: A Neglected Killer Continues on Its Rampage.

International audienc

Histoplasmose disseminada na América do Sul, o elefante invisível Central e o ponto cego letal de organizações internacionais de saúde

Centre Hospitalier de Cayenne. Centre d Investigation Clinique Antilles. Guyane, Cayenne.Centre Hospitalier de Cayenne. Centre d Investigation Clinique Antilles. Guyane, Cayenne.Asociación de Salud Integral. Clínica Familiar "Luis Angel García". Guatemala.Asociación de Salud Integral. Clínica Familiar "Luis Angel García". Guatemala.Asociación de Salud Integral. Clínica Familiar "Luis Angel García". Guatemala.Universidad Rosario. School of Medicine and Health Sciences. Bogotá and Corporacion para Investigationnes Biologicas. Bogotá, Colombia.Universidad Rosario. School of Medicine and Health Sciences. Bogotá and Corporacion para Investigationnes Biologicas. Bogotá, ColombiaUniversidad Rosario. School of Medicine and Health Sciences. Bogotá and Corporacion para Investigationnes Biologicas. Bogotá, Colombia.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Clínica Evandro Chagas. Laboratório de Micologia. Rio de Janeiro, RJ, Brazil.Federal University of Ceará. Fortaleza, CE, Brazil.Laboratório Central de Saúde Pública do Amapá. Macapá, AP, Brazil.Laboratório Central de Saúde Pública do Amapá. Macapá, AP, Brazil.Laboratório Central de Saúde Pública do Amapá. Macapá, AP, Brazil.Academic Hospital Paramaribo. Suriname.Diakonessenhuis. Utrecht, KE.Academic Hospital Paramaribo. Suriname.Academic Hospital Paramaribo. Suriname.Université de Guyane. Ecosystèmes Amazoniens et Pathologie Tropicale. French Guiana.Université de Guyane. Ecosystèmes Amazoniens et Pathologie Tropicale. French Guiana.Université de Guyane. Ecosystèmes Amazoniens et Pathologie Tropicale. French Guiana.Centre Hospitalier de l Ouest Guyanais. Service de Médecine. French Guiana.Instituto Nacional de Higiene Rafael Rangel. Departamento de Micología. Caracas, Venezuela.Instituto Nacional de Higiene Rafael Rangel. Departamento de Micología. Caracas, Venezuela.INEI-ANLIS "Dr Carlos G. Malbran". Buenos Aires, Argentina.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Rio de Janeiro, RJ, Brazil.Université de Guyane. Ecosystèmes Amazoniens et Pathologie Tropicale. French Guiana.Universidad Rosario. School of Medicine and Health Sciences. Bogotá and Corporacion para Investigationnes Biologicas. Bogotá, Colombia

International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN