This article describes the characteristics of 574 deaths associated with pandemic H1N1 influenza up to 16 July 2009. Data (except from Canada and Australia) suggest that the elderly may to some extent be protected from infection. There was underlying disease in at least half of the fatal cases. Two risk factors seem of particular importance: pregnancy and metabolic condition (including obesity which has not been considered as risk factor in previous pandemics or seasonal influenza).

T h e 2 0 0 9 p a n d e m i c h 1 n 1 i n f l u e n z a a n d i n d i g e n o u s p o p u l aT i o n s o f T h e a m e r i c a s a n d T h e p a c i f i c

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

Internet biosurveillance utilizes unstructured data from diverse web-based sources to provide early warning and situational awareness of public health threats. The scope of source coverage ranges from local media in the vernacular to international media in widely read languages. Internet biosurveillance is a timely modality that is available to government and public health officials, healthcare workers, and the public and private sector, serving as a real-time complementary approach to traditional indicator-based public health disease surveillance methods. Internet biosurveillance also supports the broader activity of epidemic intelligence. This overview covers the current state of the field of Internet biosurveillance, and provides a perspective on the future of the field.

BackgroundInternet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.Method and FindingsInfectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables.ConclusionAlthough differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.

Background: In developing countries, knowledge of antimicrobial resistance patterns is essential to define empirical therapy. Methodology: All the surgery and intensive care wards of two hospitals in Antananarivo were included to study the antimicrobial susceptibility of the pathogenic bacteria causing nosocomial infections. A repeated cross-sectional survey was conducted between September 2006 and March 2008, one day per week. Isolates were identified using classical methods, and resistance to antibiotics was assessed according to the recommendations of the Antibiogram Committee of the French Microbiology Society. Results: Clinical specimens from 706 from 651 patients were collected. Of the 533 bacterial pathogens, 46.7% were Enterobacteriaceae, 19.3% were Staphylococcus aureus, and 19.1% were pathogens from the hospital environment (Pseudomonas aeruginosa and Acinetobacter baumannii).Frequencies of resistance were high, particularly in Enterobacteriaceae; however, the rate of Staphylococcus aureus isolates resistant to oxacillin (13.6 %) was moderate and all these isolates were susceptible to glycopeptids. The percentages of isolates susceptible to ceftazidim were 81.8% for E. coli, 60.9% for Klebsiella, and 52.5% for Enterobacter spp. Resistance to third-generation cephalosporins was due to extended spectrum betalactamases (ESBL). Multivariate analysis showed that diabetes (adjusted OR: 3.9) and use of an invasive procedures (adjusted OR: 3.5) were independent risk factors for resistance to third-generation cephalosporins. Conclusion: A nationwide surveillance programme is needed to monitor the microbial trends and antimicrobial resistance in Madagascar.

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Comorbidities might predict presence of specific fungal genotypes.