Evaluating heterogeneity in cumulative meta-analyses

BACKGROUND: Recently developed measures such as I(2 )and H allow the evaluation of the impact of heterogeneity in conventional meta-analyses. There has been no examination of the development of heterogeneity in the context of a cumulative meta-analysis. METHODS: Cumulative meta-analyses of five smoking cessation interventions (clonidine, nicotine replacement therapy using gum and patch, physician advice and acupuncture) were used to calculate I(2 )and H. These values were plotted by year of publication, control event rate and sample size to trace the development of heterogeneity over these covariates. RESULTS: The cumulative evaluation of heterogeneity varied according to the measure of heterogeneity used and the basis of cumulation. Plots produced from the calculations revealed areas of heterogeneity useful in the consideration of potential sources for further study. CONCLUSION: The examination of heterogeneity in conjunction with summary effect estimates in a cumulative meta-analysis offered valuable insight into the evolution of variation. Such information is not available in the context of conventional meta-analysis and has the potential to lead to the development of a richer picture of the effectiveness of interventions

Healthcare Funding Decisions and Real-World Benefits: Reducing Bias by Matching Untreated Patients

Governments and health insurers often make funding decisions based on health gains from randomised controlled trials. These decisions are inherently uncertain because health gains in trials may not translate to practice owing to differences in the population, treatment use and setting. Post-market analysis of real-world data can provide additional evidence but estimates from standard matching methods may be biased when unobserved characteristics explain whether a patient is treated and their outcomes. We propose a new untreated matching approach that can reduce this bias. Our approach utilises the outcomes of contemporaneous untreated patients to improve the matching of treated and historical control patients. We assess the performance of this new approach compared to standard matching using a simulation study and demonstrate the steps required using a funding decision for prostate cancer treatments in Australia. Our simulation study shows that our new matching approach eliminates nearly all bias when unobserved treatment selection is related to outcomes, and outperforms standard matching in most scenarios. In our empirical example, standard matching overestimated survival by 15% (95% confidence interval 2–34) compared to our untreated matching approach. The health gains estimated using our approach were slightly lower than expected based on the trial evidence, but we also found evidence that in practice prescribers ceased prior therapies earlier, treated a more vulnerable population and continued treatment for longer. Our untreated matching approach offers researchers a new tool for reducing uncertainty in healthcare funding decisions using real-world data.The Centre for Health Economics, Monash University funded this paper

The 6-PACK programme to decrease falls and fall-related injuries in acute hospitals : protocol for an economic evaluation alongside a cluster randomised controlled trial

Background Falls are a common hospital occurrence complicating the care of patients. From an economic perspective, the impact of in-hospital falls and related injuries is substantial. However, few studies have examined the economic implications of falls prevention interventions in an acute care setting. The 6-PACK programme is a targeted nurse delivered falls prevention programme designed specifically for acute hospital wards. It includes a risk assessment tool and six simple strategies that nurses apply to patients classified as high-risk by the tool. Objective To examine the incremental cost-effectiveness of the 6-PACK programme for the prevention of falls and fall-related injuries, compared with usual care practice, from an acute hospital perspective. Methods and design The 6-PACK project is a multicentre cluster randomised controlled trial (RCT) that includes 24 acute medical and surgical wards from six hospitals in Australia to investigate the efficacy of the 6-PACK programme. This economic evaluation will be conducted alongside the 6-PACK cluster RCT. Outcome and hospitalisation cost data will be prospectively collected on approximately 16 000 patients admitted to the participating wards during the 12-month trial period. The results of the economic evaluation will be expressed as ‘cost or saving per fall prevented’ and ‘cost or saving per fall-related injury prevented’ calculated from differences in mean costs and effects in the intervention and control groups, to generate an incremental cost-effectiveness ratio (ICER). Discussion This economic evaluation will provide an opportunity to explore the cost-effectiveness of a targeted nurse delivered falls prevention programme for reducing in-hospital falls and fall-related injuries. This protocol provides a detailed statement of a planned economic evaluation conducted alongside a cluster RCT to investigate the efficacy of the 6-PACK programme to prevent falls and fall-related injuries

The 6-PACK programme to decrease fall-related injuries in acute hospitals: protocol for a cluster randomised controlled trial

Background and aims In-hospital fall-related injuries are a source of personal harm, preventable hospitalisation costs, and access block through increased length of stay. Despite increased fall prevention awareness and activity over the last decade, rates of reported fall-related fractures in hospitals appear not to have decreased. This cluster randomised controlled trial (RCT) aims to determine the efficacy of the 6-PACK programme for preventing fall-related injuries, and its generalisability to other acute hospitals.Methods 24 acute medical and surgical wards from six to eight hospitals throughout Australia will be recruited for the study. Wards will be matched by type and fall-related injury rates, then randomly allocated to the 6-PACK intervention (12 wards) or usual care control group (12 wards). The 6-PACK programme includes a nine-item fall risk assessment and six nursing interventions: &lsquo;falls alert&rsquo; sign; supervision of patients in the bathroom; ensuring patient&rsquo;s walking aids are within reach; establishment of a toileting regime; use of a low-low bed; and use of bed/chair alarm. Intervention wards will be supported by a structured implementation strategy. The primary outcomes are fall and fall-related injury rates 12 months following 6-PACK implementation.Discussion This study will involve approximately 16 000 patients, and as such is planned to be the largest hospital fall prevention RCT to be undertaken and the first to be powered for the important outcome of fall-related injuries. If effective, there is potential to implement the programme widely as part of daily patient care in acute hospital wards where fall-related injuries are a problem.<br /

In-hospital falls and fall-related injuries: a protocol for a cost of fall study.

BACKGROUND: In-hospital falls are common and pose significant economic burden on the healthcare system. To date, few studies have quantified the additional cost of hospitalisation associated with an in-hospital fall or fall-related injury. The aim of this study is to determine the additional length of stay and hospitalisation costs associated with in-hospital falls and fall-related injuries, from the acute hospital perspective.METHODS AND DESIGN: A multisite prospective study will be conducted as part of a larger falls-prevention clinical trial—the 6-PACK project. This study will involve 12 acute medical and surgical wards from six hospitals across Australia. Patient and admission characteristics, outcome and hospitalisation cost data will be prospectively collected on approximately 15 000 patients during the 15-month study period. A review of all in-hospital fall events will be conducted using a multimodal method (medical record review and daily verbal report from the nurse unit manager, triangulated with falls recorded in the hospital incident reporting and administrative database), to ensure complete case ascertainment. Hospital clinical costing data will be used to calculate patient-level hospitalisation costs incurred by a patient during their inpatient stay. Additional hospital and hospital resource utilisation costs attributable to in-hospital falls and fall-related injuries will be calculated using linear regression modelling, adjusting for a priori-defined potential confounding factors.DISCUSSION: This protocol provides the detailed statement of the planned analysis. The results from this study will be used to support healthcare planning, policy making and allocation of funding relating to falls prevention within acute hospitals