ABSTRACT
OBJECTIVE: The rapidly growing number of people in prison in Australia, combined with the high prevalence of mental disorder in this population, results in high demand for mental health services in prison settings. Despite their critical role as part of a national mental health response, prison mental health services (PMHS) in Australia have been poorly characterised. In this paper, we describe findings of the first national survey of PMHS in Australia. METHODS: We distributed a survey to key representatives of PMHS in all Australian states and territories in 2016. RESULTS: Our method constitutes a replicable process for quantifying and comparing PMHS in Australia. We describe the structure, governance and staffing models in seven jurisdictions. When compared against international recommendations, only one Australian jurisdiction (the ACT) is funded to provide services at a level equivalent to mental health services provided in the community. CONCLUSION: Prison mental health services in Australia are delivered by a complex mix of government, private sector and non-government services. Services appear to be severely under-resourced when compared with the available benchmarks.
Subject(s)
Health Personnel/statistics & numerical data , Mental Disorders/therapy , Mental Health Services/statistics & numerical data , Personnel Staffing and Scheduling/organization & administration , Prisoners/psychology , Australia , Benchmarking , Health Services Needs and Demand , Humans , Mental Health Services/organization & administration , WorkforceABSTRACT
BACKGROUND: There is growing interest in the role of hepatic steatosis in liver injury. The current standard for steatosis assessment is histological grading, although there is variability in the scoring systems used. AIMS: The aim of this study was to compare steatosis assessment by image analysis and histological grading. METHODS: Three methods were used to measure steatosis: histological grading (from 0 to 4); estimation of the percentage of hepatocytes (to nearest 5%) with steatosis; and computer-assisted image analysis. Image analysis was performed on multiple fields for each biopsy with image pro plus 4.5, with steatotic droplets identified on the basis of shape, colour and size. Computer-selected objects were reviewed to ensure that these were steatotic droplets. The predictive accuracy of the three techniques was assessed using measures of obesity and insulin resistance (homeostasis model assessment) as the outcome variables. RESULTS: There was a strong correlation between the results of image analysis and histological grade (r(s)=0.89, P<0.01), and estimated per cent steatosis (r(s)=0.93, P<0.01). The variability in the area of steatosis calculated by image analysis in different fields of a biopsy correlated with the total steatosis area (r(s)=0.93, P<0.01). CONCLUSIONS: Image analysis did not offer any additional predictive value when the association between degree of obesity or insulin resistance was correlated with the different methods of assessing steatosis. Image analysis allows measurement of area of steatosis in liver biopsy material and generates a continuous variable that facilitates statistical analysis. These aspects may prove beneficial in research settings.