Implementation of genome-scale sequencing in clinical care has significant challenges: the technology is highly dimensional with many kinds of potential results, results interpretation and delivery require expertise and coordination across multiple medical specialties, clinical utility may be uncertain, and there may be broader familial or societal implications beyond the individual participant. Transdisciplinary consortia and collaborative team science are well poised to address these challenges. However, understanding the complex web of organizational, institutional, physical, environmental, technologic, and other political and societal factors that influence the effectiveness of consortia is understudied. We describe our experience working in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, a multi-institutional translational genomics consortium.

A key aspect of the CSER consortium was the juxtaposition of site-specific measures with the need to identify consensus measures related to clinical utility and to create a core set of harmonized measures. During this harmonization process, we sought to minimize participant burden, accommodate project-specific choices, and use validated measures that allow data sharing.

Identifying platforms to ensure swift communication between teams and management of materials and data were essential to our harmonization efforts. Funding agencies can help consortia by clarifying key study design elements across projects during the proposal preparation phase and by providing a framework for data sharing data across participating projects.

In summary, time and resources must be devoted to developing and implementing collaborative practices as preparatory work at the beginning of project timelines to improve the effectiveness of research consortia.

Cardiovascular risk prediction tools are important for cardiovascular disease (CVD) prevention, however, which algorithms are appropriate for people with severe mental illness (SMI) is unclear.

To determine the cost-effectiveness using the net monetary benefit (NMB) approach of two bespoke SMI-specific risk algorithms compared to standard risk algorithms for primary CVD prevention in those with SMI, from an NHS perspective.

A microsimulation model was populated with 1000 individuals with SMI from The Health Improvement Network Database, aged 30–74 years without CVD. Four cardiovascular risk algorithms were assessed; (1) general population lipid, (2) general population BMI, (3) SMI-specific lipid and (4) SMI-specific BMI, compared against no algorithm. At baseline, each cardiovascular risk algorithm was applied and those high-risk (> 10%) were assumed to be prescribed statin therapy, others received usual care. Individuals entered the model in a ‘healthy’ free of CVD health state and with each year could retain their current health state, have cardiovascular events (non-fatal/fatal) or die from other causes according to transition probabilities.

The SMI-specific BMI and general population lipid algorithms had the highest NMB of the four algorithms resulting in 12 additional QALYs and a cost saving of approximately £37,000 (US$ 58,000) per 1000 patients with SMI over 10 years.

The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of a SMI-specific BMI algorithm (blood tests not required) makes it an attractive algorithm to implement in clinical settings.

The authors have not supplied their declaration of competing interest.

This study aimed to monitor the microbiological effect of cleaning near-patient sites over a 48-hour period with a novel disinfectant, electrolyzed water.

One ward dedicated to acute care of the elderly population in a district general hospital in Scotland.

Lockers, left and right cotsides, and overbed tables in 30 bed spaces were screened for aerobic colony count (ACC), methicillin-susceptible Staphylococcus aureus (MSSA), and methicillin-resistant S. aureus (MRSA) before cleaning with electrolyzed water. Sites were rescreened at varying intervals from 1 to 48 hours after cleaning. Microbial growth was quantified as colony-forming units (CFUs) per square centimeter and presence or absence of MSSA and MRSA at each site. The study was repeated 3 times at monthly intervals.

There was an early and significant reduction in average ACC (360 sampled sites) from a before-cleaning level of 4.3 to 1.65 CFU/cm2 at 1 hour after disinfectant cleaning (P <.0001). Average counts then increased to 3.53 CFU/cm2 at 24 hours and 3.68 CFU/cm2 at 48 hours. Total MSSA/MRSA (34 isolates) decreased by 71% at 4 hours after cleaning but then increased to 155% (53 isolates) of precleaning levels at 24 hours.

Cleaning with electrolyzed water reduced ACC and staphylococci on surfaces beside patients. ACC remained below precleaning levels at 48 hours, but MSSA/MRSA counts exceeded original levels at 24 hours after cleaning. Although disinfectant cleaning quickly reduces bioburden, additional investigation is required to clarify the reasons for rebound contamination of pathogens at near-patient sites.

Infect Control Hosp Epidemiol 2014;35(12):1505–1510

The prevention of depression is a key public health policy priority. PredictD is the first risk algorithm for the prediction of the onset of major depression. Our aim in this study was to model the cost-effectiveness of PredictD in depression prevention in general practice (GP).

A decision analytical model was developed to determine the cost-effectiveness of two approaches, each of which was compared to treatment as usual (TAU) over 12 months: (1) the PredictD risk algorithm plus a low-intensity depression prevention programme; and (2) a universal prevention programme in which there was no initial identification of those at risk. The model simulates the incidence of depression and disease progression over 12 months and calculates the net monetary benefit (NMB) from the National Health Service (NHS) perspective.

Providing patients with PredictD and a depression prevention programme prevented 15 (17%) cases of depression in a cohort of 1000 patients over 12 months and had the highest probability of being the optimal choice at a willingness to pay (WTP) of £20 000 for a quality-adjusted life year (QALY). Universal prevention was strongly dominated by PredictD plus a depression prevention programme in that universal prevention resulted in less QALYs than PredictD plus prevention for a greater cost.

Using PredictD to identify primary-care patients at high risk of depression and providing them with a low-intensity prevention programme is potentially cost-effective at a WTP of £20 000 per QALY.