Laura Ginnelly

Prevalence of working smoke alarms in local authority inner city housing: randomised controlled trial

Abstract Objectives: To identify which type of smoke alarm is most likely to remain working in local authority inner city housing, and to identify an alarm tolerated in households with smokers. Design: Randomised controlled trial. Setting:Two local authority housing estates in inner London. Participants: 2145 households. Intervention: Installation of one of five types of smoke alarm (ionisation sensor with a zinc battery; ionisation sensor with a zinc battery and pause button; ionisation sensor with a lithium battery and pause button; optical sensor with a lithium battery; or optical sensor with a zinc battery). Main outcome measure: Percentage of homes with any working alarm and percentage in which the alarm installed for this study was working after 15 months. Results: 54.4% (1166/2145) of all households and 45.9% (465/1012) of households occupied by smokers had a working smoke alarm. Ionisation sensor, lithium battery, and there being a smoker in the household were independently associated with whether an alarm was working (adjusted odds ratios 2.24 (95% confidence interval 1.75 to 2.87), 2.20 (1.77 to 2.75), and 0.62 (0.52 to 0.74)). The most common reasons for non-function were missing battery (19%), missing alarm (17%), and battery disconnected (4%). Conclusions: Nearly half of the alarms installed were not working when tested 15 months later. Type of alarm and power source are important determinants of whether a household had a working alarm. What is already known on this topic Functioning smoke alarms can reduce the risk of death in the event of a house fire Many local authorities install smoke alarms in their properties Several different types of smoke alarm are available What this study adds Only half of the smoke alarms installed in local authority housing were still working 15 months later Ionising smoke alarms with long life lithium batteries were most likely to remain functioning Installing smoke alarms may not be an effective use of resources

Using value of information analysis to inform publicly funded research priorities

IntroductionThe purpose of this article is to demonstrate the application and feasibility of using value of information analysis to help set priorities for research as part of the UK National Health Service (NHS) Health Technology Assessment Programme. Probabilistic decision analysis and value of information methods were applied to a research topic under consideration by the National Coordinating Centre for Health Technology Assessment (NCCHTA), in the UK. The case study presented considers whether long-term, low-dose antibacterial treatment of recurrent urinary tract infections (UTIs) in children is effective and cost effective compared with short-term antibacterial therapy.MethodsA probabilistic decision-analytic model was developed, within which evidence from published sources was synthesised. Eight subgroups were considered and defined in terms of sex and presence of vesico-ureteral reflux (VUR). Costs were assessed from an NHS perspective, and benefits were expressed as quality-adjusted life-years (QALYs). Simulation methods were used to determine the probability that alternative therapies would be cost effective at a range of threshold values that the NHS may attach to an additional QALY. Value of information analysis was used to quantify the cost of uncertainty associated with the decision about which therapy to adopt, which indicates the maximum value of future research. The feasibility and practicality of using value of information methods to help inform research prioritisation was evaluated.ResultsAt a threshold value for an additional QALY of £30 000, long-term antibacterial treatment may be regarded as cost effective for all eight patient groups. There was, however, substantial uncertainty surrounding the choice of antibacterial.Discussion/conclusionThe use of value of information methods was feasible and could inform research prioritisation for the NHS. In the context of this specific decision faced by the NHS, the results show that long-term low-dose antibacterials for preventing recurrent UTIs may be cost effective, based on current evidence. However, the analysis suggests that further primary research with longer follow-up may be worthwhile, particularly for girls with no VUR.

Populating decision-analytic models: the feasibility and efficiency of database searching for individual parameters.

OBJECTIVES The aim of the study was to investigate the feasibility and effectiveness of searching selected databases to identify information required to populate a decision-analytic model. METHODS Methods of searching for information to populate a decision-analytic model were piloted using a case study of prophylactic antibiotics to prevent recurrent urinary tract infections in children. This study explored how the information requirements for a decision-analytic model could be developed into searchable questions and how search strategies could be derived to answer these questions. The study also assessed the usefulness of three published search filters and explored which resources might produce relevant information for the various model parameters. RESULTS Based on the data requirements for this case study, 42 questions were developed for searching. These questions related to baseline event rates, health-related quality of life and outcomes, relative treatment effects, resource use and unit costs, and antibiotic resistance. A total of 1,237 records were assessed by the modeler, and of these, 48 were found to be relevant to the model. Search precision ranged from 0 percent to 38 percent, and no single database proved the most useful for all the questions. CONCLUSIONS The process of conducting specific searches to address each of the model questions provided information that was useful in populating the case study model. The most appropriate resources to search were dependent on the question, and multiple database searching using focused search strategies may prove more effective in finding relevant data than thorough searches of a single database.