Suchergebnisse

These data belong to the Royal Government of Cambodia, as part of the Dengue National Program managed by the Ministry of Health's Center for Malaria Control (vectorborne diseases). A copy of the dataset can be accessible upon justified request if approved by the two following stakeholders: Dr. Didier FONTENILLE, Director, Institut Pasteur du Cambodge (dfontenille@pasteur-kh.org), and Dr. HUY Rekol, Director, National Malaria Center, Ministry of Health (director@cnm.gov.kh) ; International audience ; Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) proj-ect's knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008-2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008-2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians' and decisionmakers' needs, is cost-free and is easy to implement at the provincial level.

These data belong to the Royal Government of Cambodia, as part of the Dengue National Program managed by the Ministry of Health's Center for Malaria Control (vectorborne diseases). A copy of the dataset can be accessible upon justified request if approved by the two following stakeholders: Dr. Didier FONTENILLE, Director, Institut Pasteur du Cambodge (dfontenille@pasteur-kh.org), and Dr. HUY Rekol, Director, National Malaria Center, Ministry of Health (director@cnm.gov.kh) ; International audience ; Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) proj-ect's knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008-2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks ...

These data belong to the Royal Government of Cambodia, as part of the Dengue National Program managed by the Ministry of Health's Center for Malaria Control (vectorborne diseases). A copy of the dataset can be accessible upon justified request if approved by the two following stakeholders: Dr. Didier FONTENILLE, Director, Institut Pasteur du Cambodge (dfontenille@pasteur-kh.org), and Dr. HUY Rekol, Director, National Malaria Center, Ministry of Health (director@cnm.gov.kh) ; International audience ; Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) proj-ect's knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008-2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008-2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians' and decisionmakers' needs, is cost-free and is easy to implement at the provincial level.

These data belong to the Royal Government of Cambodia, as part of the Dengue National Program managed by the Ministry of Health's Center for Malaria Control (vectorborne diseases). A copy of the dataset can be accessible upon justified request if approved by the two following stakeholders: Dr. Didier FONTENILLE, Director, Institut Pasteur du Cambodge (dfontenille@pasteur-kh.org), and Dr. HUY Rekol, Director, National Malaria Center, Ministry of Health (director@cnm.gov.kh) ; International audience ; Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) proj-ect's knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008-2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008-2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians' and decisionmakers' needs, is cost-free and is easy to implement at the provincial level.

BACKGROUND: Evidence on the effectiveness of low-cost, sustainable, biological vector-control tools for the Aedes mosquitoes is limited. Therefore, the purpose of this trial is to estimate the impact of guppy fish (guppies), in combination with the use of the larvicide pyriproxyfen (Sumilarv® 2MR), and Communication for Behavioral Impact (COMBI) activities to reduce entomological indices in Cambodia. METHODS/DESIGN: In this cluster randomized controlled, superiority trial, 30 clusters comprising one or more villages each (with approximately 170 households) will be allocated, in a 1:1:1 ratio, to receive either (1) three interventions (guppies, Sumilarv® 2MR, and COMBI activities), (2) two interventions (guppies and COMBI activities), or (3) control (standard vector control). Households will be invited to participate, and entomology surveys among 40 randomly selected households per cluster will be carried out quarterly. The primary outcome will be the population density of adult female Aedes mosquitoes (i.e., number per house) trapped using adult resting collections. Secondary outcome measures will include the House Index, Container Index, Breteau Index, Pupae Per House, Pupae Per Person, mosquito infection rate, guppy fish coverage, Sumilarv® 2MR coverage, and percentage of respondents with knowledge about Aedes mosquitoes causing dengue. In the primary analysis, adult female Aedes density and mosquito infection rates will be aggregated over follow-up time points to give a single rate per cluster. This will be analyzed by negative binomial regression, yielding density ratios. DISCUSSION: This trial is expected to provide robust estimates of the intervention effect. A rigorous evaluation of these vector-control interventions is vital to developing an evidence-based dengue control strategy and to help direct government resources. TRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN85307778 . Registered on 25 October 2015.

Southeast Asia (SEA) emerged relatively unscathed from the first year of the global SARS-CoV-2 pandemic, but as of July 2021 the region is experiencing a surge in case numbers primarily driven by Alpha (B.1.1.7) and subsequently the more transmissible Delta (B.1.617.2) variants. While initial disease burden was mitigated by swift government responses, favorable cultural and societal factors, the more recent rise in cases suggests an under-appreciation of prior prevalence and over-appreciation of possible cross-protective immunity from exposure to endemic viruses, and highlights the effects of vaccine rollout at varying tempos and of variable efficacy. This burgeoning crisis is further complicated by co-existence of malaria and dengue in the region, with implications of serological cross-reactivity on interpretation of SARS-CoV-2 assays and competing resource demands impacting efforts to contain both endemic and pandemic disease.

National Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures. Samples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs. GenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs. GenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community. The GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the National Institute of Allergy and Infectious Diseases.