IntroductionHealth authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO).MethodsFor 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient.ResultsThe median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95th percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations.ConclusionsThresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.
BackgroundBuruli ulcer (BU), caused by Mycobacterium ulcerans, is increasing in incidence in Victoria, Australia. To improve understanding of disease transmission, we aimed to map the location of BU lesions on the human body.MethodsUsing notification data and clinical records review, we conducted a retrospective observational study of patients diagnosed with BU in Victoria from 1998–2015. We created electronic density maps of lesion locations using spatial analysis software and compared lesion distribution by age, gender, presence of multiple lesions and month of infection.FindingsWe examined 579 patients with 649 lesions; 32 (5.5%) patients had multiple lesions. Lesions were predominantly located on lower (70.0%) and upper (27.1%) limbs, and showed a non-random distribution with strong predilection for the ankles, elbows and calves. When stratified by gender, upper limb lesions were more common (OR 1·97, 95% CI 1·38–2·82, p<0·001) while lower limb lesions were less common in men than in women (OR 0·48, 95% CI 0·34–0·68, p<0·001). Patients aged ≥ 65 years (OR 3·13, 95% CI 1·52–6·43, p = 0·001) and those with a lesion on the ankle (OR 2·49, 95% CI 1·14–5·43, p = 0·02) were more likely to have multiple lesions. Most infections (71.3%) were likely acquired in the warmer 6 months of the year.InterpretationComparison with published work in Cameroon, Africa, showed similar lesion distribution and suggests the mode of M. ulcerans transmission may be the same across the globe. Our findings also aid clinical diagnosis and provide quantitative background information for further research investigating disease transmission.
Since 2000, cases of the neglected tropical disease Buruli ulcer, caused by infection with , have increased 100-fold around Melbourne (population 4.4 million), the capital of Victoria, in temperate southeastern Australia. The reasons for this increase are unclear. Here, we used whole-genome sequence comparisons of 178 isolates obtained primarily from human clinical specimens, spanning 70 years, to model the population dynamics of this pathogen from this region. Using phylogeographic and advanced Bayesian phylogenetic approaches, we found that there has been a migration of the pathogen from the east end of the state, beginning in the 1980s, 300 km west to the major human population center around Melbourne. This move was then followed by a significant increase in population size. These analyses inform our thinking around Buruli ulcer transmission and control, indicating that is introduced to a new environment and then expands, rather than it being from the awakening of a quiescent pathogen reservoir. Buruli ulcer is a destructive skin and soft tissue infection caused by and is characterized by progressive skin ulceration, which can lead to permanent disfigurement and long-term disability. Despite the majority of disease burden occurring in regions of West and central Africa, Buruli ulcer is also becoming increasingly common in southeastern Australia. Major impediments to controlling disease spread are incomplete understandings of the environmental reservoirs and modes of transmission of The significance of our research is that we used genomics to assess the population structure of this pathogen at the Australian continental scale. We have then reconstructed a historical bacterial spread and modeled demographic dynamics to reveal bacterial population expansion across southeastern Australia. These findings provide explanations for the observed epidemiological trends with Buruli ulcer and suggest possible management to control disease spread.
BackgroundBuruli ulcer (BU) is a geographically-restricted infection caused by Mycobacterium ulcerans; contact with an endemic region is the primary risk factor for disease acquisition. Globally, efforts to estimate the incubation period of BU are often hindered as most patients reside permanently in endemic areas. However, in the south-eastern Australian state of Victoria, a significant proportion of people who acquire BU are visitors to endemic regions. During a sustained outbreak of BU on the Bellarine peninsula we estimated a mean incubation period of 4.5 months. Since then cases on the Bellarine peninsula have declined but a new endemic area has developed centred on the Mornington peninsula.MethodRetrospective review of 443 cases of BU notified in Victoria between 2013 and 2016. Telephone interviews were performed to identify all cases with a single visit to an endemic region, or multiple visits within a one month period. The incubation period was defined as the time between exposure to an endemic region and symptom onset. Data were subsequently combined with those from our earlier study incorporating cases from 2002 to 2012.ResultsAmong the 20 new cases identified in short-term visitors, the mean incubation period was 143 days (4.8 months), very similar to the previous estimate of 135 days (4.5 months). This was despite the predominant exposure location shifting from the Bellarine peninsula to the Mornington peninsula. We found no association between incubation period and age, sex, location of exposure, duration of exposure to an endemic region or location of BU lesion.ConclusionsOur study confirms the mean incubation period of BU in Victoria to be between 4 and 5 months. This knowledge can guide clinicians and suggests that the mode of transmission of BU is similar in different geographic regions in Victoria.
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