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Background Scabies is listed as a neglected tropical disease by the World Health Organization. Crusted scabies affects vulnerable and immunosuppressed individuals and is highly contagious because of the enormous number of Sarcoptes scabiei mites present in the hyperkeratotic skin. Undiagnosed and untreated crusted scabies cases can result in outbreaks of scabies in residential facilities and can also undermine the success of scabies mass drug administration programs. Methods and principal findings Crusted scabies became a formally notifiable disease in the Northern Territory of Australia in 2016. We conducted a 2-year prospective study of crusted scabies cases notified between March 2016 and February 2018, with subsequent follow up for 22 months. Demographics, clinical and laboratory data, treatment and outcomes were analysed, with cases classified by severity of disease. Over the 2-year study period, 80 patients had 92 episodes of crusted scabies; 35 (38%) were Grade 1 crusted scabies, 36 (39%) Grade 2 and 21 (23%) Grade 3. Median age was 47 years, 47 (59%) were female, 76 (95%) Indigenous Australians and 57 (71%) from remote Indigenous communities. Half the patients were diabetic and 18 (23%) were on dialysis for end-stage kidney failure. Thirteen (16%) patients had no comorbidities, and these were more likely to have Grade 3 disease. Eosinophilia was present in 60% and high immunoglobulin E in 94%. Bacteremia occurred in 11 episodes resulting in one fatality with methicillin-susceptible Staphylococcus aureus bacteremia. Two other deaths occurred during admission and 10 others died subsequent to discharge consequent to comorbidities. Treatment generally followed the recommended guidelines, with 3, 5 or 7 doses of oral ivermectin depending on the documented grade of crusted scabies, together with daily alternating topical scabicides and topical keratolytic cream. While response to this therapy was usually excellent, there were 33 episodes of recurrent crusted scabies with the majority attributed to new infection subsequent to return to a scabies-endemic community. Conclusions Crusted scabies can be successfully treated with aggressive guideline-based therapy, but high mortality remains from underlying comorbidities. Reinfection on return to community is common while scabies remains endemic.

Background: Echocardiographic screening can detect asymptomatic cases of rheumatic heart disease (RHD), facilitating access to treatment. Barriers to implementation of echocardiographic screening include the requirement for expensive equipment and expert practitioners. We aimed to evaluate the diagnostic accuracy of an abbreviated echocardiographic screening protocol (single parasternal-long-axis view with a sweep of the heart) performed by briefly trained, nonexpert practitioners using handheld ultrasound devices. Methods: Participants aged 5 to 20 years in Timor-Leste and the Northern Territory of Australia had 2 echocardiograms: one performed by an expert echocardiographer using a GE Vivid I or Vivid Q portable ultrasound device (reference test), and one performed by a nonexpert practitioner using a GE Vscan handheld ultrasound device (index test). The accuracy of the index test, compared with the reference test, for identifying cases with definite or borderline RHD was determined. Results: There were 3111 enrolled participants; 2573 had both an index test and reference test. Median age was 12 years (interquartile range, 10–15); 58.2% were female. Proportion with definite or borderline RHD was 5.52% (95% CI, 4.70–6.47); proportion with definite RHD was 3.23% (95% CI, 2.61–3.98). Compared with the reference test, sensitivity of the index test for definite or borderline RHD was 70.4% (95% CI, 62.2–77.8), specificity was 78.1% (95% CI, 76.4–79.8). Conclusions: Nonexpert practitioners can be trained to perform single parasternal-long-axis view with a sweep of the heart echocardiography. However, the specificity and sensitivity are inadequate for echocardiographic screening. Improved training for nonexpert practitioners should be investigated.

Background Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of criteria comprising clinical, laboratory, electrocardiographic and echocardiographic findings. The complexity of the algorithm and the fact that clinicians lack familiarity with ARF, make ARF diagnosis ideally suited to an electronic decision support tool. The ARF Diagnosis Calculator was developed to assist clinicians in diagnosing ARF and correctly assign categories of ‘possible, ‘probable’ or ‘definite’ ARF. This research aimed to evaluate the acceptability, accuracy, and test performance of the ARF Diagnosis Calculator. Methods Three strategies were used to provide triangulation of data. Users of the calculator employed at Top End Health Service, Northern Territory, Australia were invited to participate in an online survey, and clinicians with ARF expertise were invited to participate in semi-structured interviews. Qualitative data were analysed using inductive analysis. Performance of the calculator in correctly diagnosing ARF was assessed using clinical data from 35 patients presenting with suspected ARF. Diagnoses obtained from the calculator were compared using the Kappa statistic with those obtained from a panel of expert clinicians. Results Survey responses were available from 23 Top End Health Service medical practitioners, and interview data were available from five expert clinicians. Using a 6-point Likert scale, participants highly recommended the ARF Diagnosis Calculator (median 6, IQR 1), found it easy to use (median 5, IQR 1) and believed the calculator helped them diagnose ARF (median 5, IQR 1). Clinicians with ARF expertise noted that electronic decision making is not a substitute for clinical experience. There was high agreement between the ARF Diagnosis Calculator and the ‘gold standard’ ARF diagnostic process (κ = 0.767, 95% CI: 0.568–0.967). Incorrect assignment of diagnosis occurred in 4/35 (11%) patients highlighting the greater accuracy of expert clinical input for ambiguous presentations. Sixteen changes were incorporated into a revised version of the calculator. Conclusions The ARF Diagnosis Calculator is an easy-to-use, accessible tool, but it does not replace clinical expertise. The calculator performed well amongst clinicians and is an acceptable tool for use within the clinical setting with a high level of accuracy in comparison to the gold standard diagnostic process. Effective resources to support clinicians are critically important for improving the quality of care of ARF.

Melioidosis is an infectious disease caused by the bacterium Burkholderia pseudomallei. Although this environmental organism is endemic in certain regions of Australia, it is not considered endemic in Southern Queensland, where the last case was reported 21 years ago. We report a climate change–associated outbreak of melioidosis occurring during two La Niña events in a region previously considered nonendemic for B. pseudomallei. During a 15-month period, 14 cases of locally acquired melioidosis were identified. Twelve patients were adults (> 50 years), with diabetes mellitus the most common risk factor in 6 of 12 patients (50%). Eleven patients (79%) had direct exposure to floodwaters or the flooded environment. This study suggests an association between climate change and an increased incidence of melioidosis. In addition, this is the first report of environmental sampling and whole-genome analysis to prove endemicity and local acquisition in this region.

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BACKGROUND Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of criteria comprising clinical, laboratory, electrocardiographic and echocardiographic findings. The complexity of the algorithm and the fact that clinicians lack familiarity with ARF, make ARF diagnosis ideally suited to an electronic decision support tool. We developed an ARF Diagnosis Calculator to assist clinicians in diagnosing ARF and correctly assigning categories of ‘possible, ‘probable’ or ‘definite’ ARF. OBJECTIVE To evaluate the acceptability and accuracy of the ARF Diagnosis Calculator as perceived by clinicians in Northern Australia where ARF rates are high, and test performance against a ‘gold standard’. METHODS Three strategies were used to provide triangulation of data. Users of the calculator employed at Top End Health Service, Northern Territory, Australia were invited to participate in an online survey about the calculator, and clinicians with ARF expertise were invited to participate in semi-structured interviews. Qualitative data were analysed using inductive analysis. Performance of the calculator in correctly assigning a diagnosis of possible, probable or definite ARF, or not ARF, was assessed using clinical data from 35 patients presenting with suspected ARF. Diagnoses obtained from the calculator were compared using the Kappa statistic with those obtained from a panel of expert clinicians, considered the ‘gold standard’. Findings were shared with developers of the calculator and changes were incorporated. RESULTS Survey responses were available from 23 Top End Health Service medical practitioners, and interview data were available from five expert clinicians. Using a 6-point Likert scale, participants highly recommended the ARF Diagnosis Calculator (median score 6, IQR 1) and found it easy to use (median 5, IQR 1). Participants believed the calculator helped them diagnose ARF (median 5, IQR 1). Valued features included educational content and laboratory test reference ranges. Criticisms included: too many pop-up messages to be clicked through; that it is less helpful in remote areas which lack access to investigation results; and the need for more clarity about actively excluding alternative diagnoses to avoid false-positive ARF diagnoses. Importantly, clinicians with ARF expertise noted that electronic decision making is not a substitute for clinical experience. There was high agreement between the ARF Diagnosis Calculator and the ‘gold standard’ ARF diagnostic process (κ=0.767, 95% CI: 0.568-0.967). However, incorrect assignment of diagnosis occurred in 4/35 (11%) patients highlighting the greater accuracy of expert clinical input for ambiguous presentations. Sixteen changes were incorporated into a revised version of the calculator. CONCLUSIONS The ARF Diagnosis Calculator is an easy-to-use, accessible tool, but it does not replace clinical expertise. Effective resources to support clinicians in diagnosing and managing ARF are critically important for improving the quality of care of ARF.