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Objectives To evaluate the cost effectiveness of a community-delivered consultation aimed at improving infant sleep and maternal well-being. Methods A decision-analytic model was developed that compared the costs and benefits of an infant sleep consultation with usual care. The effectiveness of the consultation was based on clinical evidence, and improvements in maternal quality of life were estimated by mapping the Edinburgh Postnatal Depression Scale scores to published utility scores. Cost effectiveness was calculated as the incremental cost per quality-adjusted life-year gained (QALY). Results The statistically significant improvements in mean Edinburgh Postnatal Depression Scale scores at 4- and 16-month follow-ups were used to estimate the benefit in terms of QALYs. The modeled results demonstrated that the infant sleep consultation is low-cost (A$ 436), more effective in terms of QALYs gained (0.017), and cost-effective. The estimated incremental cost-effectiveness ratio was A$ 4031/QALY gained. The main drivers of the model were the use of early parenting centers and nurse training costs. Conclusions Community-based nurse-delivered infant sleep consultations aid infant sleep, improve maternal quality of life, and are cost-effective compared with usual care and lead to improvements in quality of life through a reduction in postnatal depression. [ABSTRACT FROM AUTHOR]

Statistical analysis plans describe the planned data management and analysis for clinical trials. This supports transparent reporting and interpretation of clinical trial results. This paper reports the statistical analysis plan for the RESOLVE clinical trial. The RESOLVE trial assigned participants with chronic low back pain to graded sensory-motor precision training or sham-control. We report the planned data management and analysis for the primary and secondary outcomes. The primary outcome is pain intensity at 18-weeks post randomization. We will use mixed-effects models to analyze the primary and secondary outcomes by intention-to-treat. We will report adverse effects in full. We also describe analyses if there is non-adherence to the interventions, data management procedures, and our planned reporting of results. This statistical analysis plan will minimize the potential for bias in the analysis and reporting of results from the RESOLVE trial. ACTRN12615000610538 (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368619). [ABSTRACT FROM AUTHOR]

Background: Up to 50% of heart transplant candidates require bridging with left ventricular assist devices (VAD). This study describes hospital activity and cost 1 year preceding and 1 year following VAD implant (pre-VAD) and for the year before transplant (pre-HTX). The sample comprises an Australian cohort and is the first study to investigate costs using both institutional and linked administrative data.Methods: Institutional activity was established for 77 consecutive patients actively listed for transplant between 2009 and 2012. Costs were sourced from the institution or Australian refined diagnosis groups (arDRGs) and the National Efficient Price for admissions to other public and private institutions. Data from 25/77 VAD recipients were analysed and compared with data from 52/77 pre-transplant patients. Total and per day at risk costs were assessed, as well as totals per resource.Results: Fifty per cent (50%) of the hospital costs in the pre-VAD year occurred during admission of VAD implant. Sixty-four per cent (64%) of costs in the pre-HTX and 38% in the pre-VAD period occurred outside the implanting centre. Costs in the year prior to VAD, $97,565 (IQR $86,907-$153,916), were significantly higher than costs accrued in the year prior to transplant, $40,250 ($13,493-$81,260), p < 0.0001. Once discharged, costs per day at risk for post-VAD patients approximated those from the pre-admission period, p = 0.16 and in the more clinically stable pre-HTX cohort, p = 0.08.Conclusion: Compared with the year prior, VAD implant stabilised hospital cost in patients discharged home. A high proportion of the hospital costs in the pre-implant year occur outside the implanting centre and should be considered in economic models assessing the impact of VAD implant. [ABSTRACT FROM AUTHOR]