BackgroundInfluenza is characterized by seasonal outbreaks, often with a high rate of morbidity and mortality. It is also known to be a cause of significant amount secondary bacterial infections. Streptococcus pneumoniae is the main pathogen causing secondary bacterial pneumonia after influenza and subsequently, influenza could participate in acquiring Invasive Pneumococcal Disease (IPD).MethodsIn this study, we aim to investigate the relation between influenza and IPD by estimating the yearly excess of IPD cases due to influenza. For this purpose, we use influenza periods as an indicator for influenza activity as a risk factor in subsequent analysis. The statistical modeling has been made in two modes. First, we constructed two negative binomial regression models. For each model, we estimated the contribution of influenza in the models, and calculated number of excess number of IPD cases. Also, for each model, we investigated several lag time periods between influenza and IPD. Secondly, we constructed an "influenza free" baseline, and calculated differences in IPD data (observed cases) and baseline (expected cases), in order to estimate a yearly additional number of IPD cases due to influenza. Both modes were calculated using zero to four weeks lag time.ResultsThe analysis shows a yearly increase of 72–118 IPD cases due to influenza, which corresponds to 6–10% per year or 12–20% per influenza season. Also, a lag time of one to three weeks appears to be of significant importance in the relation between IPD and influenza.ConclusionThis epidemiological study confirms the association between influenza and IPD. Furthermore, negative binomial regression models can be used to calculate number of excess cases of IPD, related to influenza.

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Background: Surveillance data allow for analysis, providing public health officials and policymakers with a basis for long-term priorities and timely information on possible outbreaks for rapid response (data for action). In this article we describe the considerations and technology behind a newly introduced public web tool in Sweden for easy retrieval of county and national surveillance data on communicable diseases.

Influenza often leads to bacterial complications that require treatment. It may also be confused with bacterial respiratory infections, leading to unnecessary prescription of antibiotics. In this first study on the relationship between influenza and antibiotic utilization for a whole country, weekly data on verified influenza cases in Sweden were compared to weekly sales of antibiotics for 5 influenza seasons 1997-2002. The peak of influenza activity occurred during the winter. In 4 out of the 5 monitored influenza seasons it occurred in February-March. The fluctuation of antibiotic utilization was relatively constant over the years with peaks before Christmas and in February-March. There were no obvious differences in the total amount of antibiotics dispensed over the years that could be related to influenza activity, but a coincidental relationship between the peaks of diagnosed influenza cases and the peaks of antibiotic utilization was indicated, especially for older age groups.

Coagulase-negative Staphylococci (CoNS) are a major cause of postoperative infections. These infections are often associated with foreign material implants and/or a compromised immune system in the patient. Multiresistant strains are increasingly common in the hospital environment and there is concern that the infections will become difficult or impossible to treat. This report is based on a study of 75 patients, with postoperative infections caused by CoNS after thoracic surgery. All patients were treated with surgical revision and antibiotic therapy. One or more bacterial cultures were made in each case, and the resistance pattern of the CoNS found was determined. The goal of the study was to evaluate possible relationships between antibiotic therapy and the appearance of resistance to antibiotics in CoNS found. To describe this relationship, three models were constructed and analyzed by multiple logistic regression. The results indicate an increased resistance to beta-lactam antibiotics and clindamycin after the use of cephalosporins. Also, the use of vancomycin or vancomycin in combination with rifampicin or fusidic acid increases the risk for development of resistance to beta-lactam antibiotics, ciprofloxacin, fusidic acid, clindamycin, netilmycin, and rifampicin. The hypothesis that a combination of antibiotics will curtail the development of resistance was not supported in this study.