Health services administration

Although mental health problems represent the largest burden of disease in young people, access to mental health care has been poor for this group

Sarah E Hetrick · Alan P Bailey · Kirsten E Smith · Ashok Malla · Steve Mathias · Swaran P Singh · Aileen O'Reilly · Swapna K Verma · Laelia Benoit · Theresa M Fleming · Marie Rose Moro · Debra J Rickwood · Joseph Duffy · Trissel Eriksen · Robert Illback · Caroline A Fisher · Patrick D McGorry

Promoting routine, consistent collection of clinical stage data may enhance appropriate targeting of mental health care delivery

Matthew P Hamilton · Sarah E Hetrick · Cathrine Mihalopoulos · David Baker · Vivienne Browne · Andrew M Chanen · Kerryn Pennell · Rosemary Purcell · Heather Stavely · Patrick D McGorry

More economic evaluations are required in youth mental health

Matthew P Hamilton · Sarah E Hetrick · Cathrine Mihalopoulos · David Baker · Vivienne Browne · Andrew M Chanen · Kerryn Pennell · Rosemary Purcell · Heather Stavely · Patrick D McGorry

WBA is a reliable method if multiple tools and assessors are used over a period of time

Balakrishnan (Kichu) R Nair · Joyce MW Moonen-van Loon · Mulavana Parvathy · Brian C Jolly · Cees PM van der Vleuten

To the Editor:High technology solutions to the difficult task of selecting and monitoring moles (pigmented skin naevi) may be useful to keep accurate records of people’s skin. Adopting military surveillance and warfare technology,1 there are computer algorithms that search for changes in moles’ appearance over time. Deep convolutional neural networks analysis can group them into benign or malignant lesions with high accuracy.2 In a study by Esteva and colleagues,2 the convolutional neural networks algorithm differentiated between benign, malignant or non-neoplastic lesions with about 72% accuracy compared with about 66% accuracy by two dermatologists; for melanocytic lesions, the algorithm had a better sensitivity and specificity performance compared with the average of 21 dermatologists, although these findings still need to be replicated in independent datasets. Despite recent advances, there are still questions about how Australians can benefit from this technology and how it is best integrated into clinical practice. Cancer agencies worldwide do not recommend screening for melanoma, but instead ask people to make skin self-examinations a habit and present to a doctor with moles of concern — although informal screening is widespread in Australia. Apps that provide easy access to personalised risk estimation may alert people to engage in such exams more frequently. Moreover, apps that guide people through the skin self-examination process may also be useful, as most people find this task complex.3 Once people notice a spot or mole, they may seek a clinical skin examination. Evidence that clinical skin exams are beneficial comes from the Queensland melanoma case control study4 and other similar studies that show that they lead to the detection of thinner melanomas. There are many apps that allow people to take and send photos of moles, but these are highly variable in sophistication and costs. Whether such technology is best placed in front of (for filtering out clearly benign lesions) or after a clinician’s diagnosis (for additional validation) is also matter of debate. Apps should not distract from the patient–doctor relationship, as the final decision about excision requires face-to-face consultations. While technology solutions are promising, validation studies have mostly been small, have lacked a control group or have not been replicated in clinical practice. Independent big research initiatives, such as the International Skin Imaging Collaboration Challenge on Skin Lesion Analysis towards Melanoma Detection,5 are underway to take the momentum further. This healthy competition may be just what is needed to take the last steps to eradicate melanoma.

Monika Janda · H Peter Soyer

To the Editor:Cadilhac and colleagues1 explore an important issue in the measurement and reporting of stroke outcomes. We agree that appropriate risk-adjustment methods are essential to compare hospital outcomes. We also agree that stroke severity is an important predictor of mortality for individual patients. However, we do not agree that determining stroke severity is essential for robust risk-adjustment approaches. The Bureau of Health Information has recently published its second report on 30-day mortality.2 Our approach includes adjustment for comorbidity, and we developed separate models for ischaemic and haemorrhagic stroke, given the significant differences in outcomes and risk factors. The approach of Cadilhac and colleagues does neither, which means that we do not know the impact of including comorbidity adjustment and severity in the same model. Further, our method is applicable to small hospitals with as few as 50 patients in a 3-year period. In settings such as Australia, where many patients reside outside major cities, it is important to assess outcomes in both small and large hospitals. The results of Cadhilac et al show that adjusting for stroke severity affected hospital rankings, and across the two models, hospital rates changed on average by 0.01 (range, 0.001–0.026). The impact on outlier status is not described. In our view, it is the outlier status that is the most salient element of public reporting, and indirect standardisation should not be used to rank hospitals.3 Measurement approaches in the United States and Canada do not adjust for severity, and it is not currently possible to do so using administrative records in Australia. While Cadhilac et al have shown that including severity information can affect rankings, such rankings are not appropriate to assess hospital performance when they are based on indirect standardisation and do not take account of hospital size.

Kim Sutherland · Jean-Frederic Levesque · Julia Chessman

In reply

Monique Kilkenny · Leonid Churilov · Dominique A Cadilhac