Limits...
Application of the development stages of a cluster randomized trial to a framework for valuating complex health interventions.

Loeb MB - BMC Health Serv Res (2002)

Bottom Line: Trials of complex health interventions often pose difficult methodologic challenges.In so doing, the utility of the framework for health services researchers is evaluated.Using the five phases of the framework (theoretical, identification of components of the intervention, definition of trial and intervention design, methodological issues for main trial, promoting effective implementation), corresponding stages in the development of the cluster randomized trial using diagnostic and treatment algorithms to optimize the use of antibiotics in nursing homes are identified and described.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, McMaster University, Hamilton, Ontario, Canada. loebm@mcmaster.ca

ABSTRACT

Introduction: Trials of complex health interventions often pose difficult methodologic challenges. The objective of this paper is to assess the extent to which the various development steps of a cluster randomized trial to optimize antibiotic use in nursing homes are represented in a recently published framework for the design and evaluation of complex health interventions. In so doing, the utility of the framework for health services researchers is evaluated.

Methods: Using the five phases of the framework (theoretical, identification of components of the intervention, definition of trial and intervention design, methodological issues for main trial, promoting effective implementation), corresponding stages in the development of the cluster randomized trial using diagnostic and treatment algorithms to optimize the use of antibiotics in nursing homes are identified and described.

Results: Synthesis of evidence needed to construct the algorithms, survey and qualitative research used to define components of the algorithms, a pilot study to assess the feasibility of delivering the algorithms, methodological issues in the main trial including choice of design, allocation concealment, outcomes, sample size calculation, and analysis are adequately represented using the stages of the framework.

Conclusions: The framework is a useful resource for researchers planning a randomized clinical trial of a complex intervention.

Show MeSH

Related in: MedlinePlus

Diagnostic algorithm. This algorithm guides physicians and nurses in the ordering of urine cultures for nursing home residents with suspected infections.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC117443&req=5

Figure 1: Diagnostic algorithm. This algorithm guides physicians and nurses in the ordering of urine cultures for nursing home residents with suspected infections.

Mentions: The first step in the development of the algorithms was to systematically review the literature for data that could lead to strategies for reducing antibiotic use. Lower respiratory infections, urinary infections, and skin and soft tissue infections account for the majority of bacterial infections in residents of long-term care facilities [1,2,4,14]. No data to support reducing antibiotics for lower respiratory or skin infections was located. In contrast, data to support reducing antibiotic use for urinary indications was found. Asymptomatic bacteriuria, the presence of bacteria in the urine in the absence of urinary symptoms, occurs in up to 50% of older institutionalized women and 35% of institutionalized older men [20]. There are four randomized controlled trials that demonstrate no benefit in treating asymptomatic bacteriuria in residents of long-term care facilities [21-24]. Despite the evidence, one third of all prescriptions for urinary indications in residents of nursing homes are for asymptomatic bacteriuria [1]. Accordingly, the algorithms indicate that urine should not be cultured and antibiotics should not be prescribed in the absence of clinical features of urinary infection (Figures 1 and 2). The algorithms also consider the increased risk of urinary infection with indwelling urinary catheters and with specific urinary symptoms [25-28].


Application of the development stages of a cluster randomized trial to a framework for valuating complex health interventions.

Loeb MB - BMC Health Serv Res (2002)

Diagnostic algorithm. This algorithm guides physicians and nurses in the ordering of urine cultures for nursing home residents with suspected infections.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC117443&req=5

Figure 1: Diagnostic algorithm. This algorithm guides physicians and nurses in the ordering of urine cultures for nursing home residents with suspected infections.
Mentions: The first step in the development of the algorithms was to systematically review the literature for data that could lead to strategies for reducing antibiotic use. Lower respiratory infections, urinary infections, and skin and soft tissue infections account for the majority of bacterial infections in residents of long-term care facilities [1,2,4,14]. No data to support reducing antibiotics for lower respiratory or skin infections was located. In contrast, data to support reducing antibiotic use for urinary indications was found. Asymptomatic bacteriuria, the presence of bacteria in the urine in the absence of urinary symptoms, occurs in up to 50% of older institutionalized women and 35% of institutionalized older men [20]. There are four randomized controlled trials that demonstrate no benefit in treating asymptomatic bacteriuria in residents of long-term care facilities [21-24]. Despite the evidence, one third of all prescriptions for urinary indications in residents of nursing homes are for asymptomatic bacteriuria [1]. Accordingly, the algorithms indicate that urine should not be cultured and antibiotics should not be prescribed in the absence of clinical features of urinary infection (Figures 1 and 2). The algorithms also consider the increased risk of urinary infection with indwelling urinary catheters and with specific urinary symptoms [25-28].

Bottom Line: Trials of complex health interventions often pose difficult methodologic challenges.In so doing, the utility of the framework for health services researchers is evaluated.Using the five phases of the framework (theoretical, identification of components of the intervention, definition of trial and intervention design, methodological issues for main trial, promoting effective implementation), corresponding stages in the development of the cluster randomized trial using diagnostic and treatment algorithms to optimize the use of antibiotics in nursing homes are identified and described.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, McMaster University, Hamilton, Ontario, Canada. loebm@mcmaster.ca

ABSTRACT

Introduction: Trials of complex health interventions often pose difficult methodologic challenges. The objective of this paper is to assess the extent to which the various development steps of a cluster randomized trial to optimize antibiotic use in nursing homes are represented in a recently published framework for the design and evaluation of complex health interventions. In so doing, the utility of the framework for health services researchers is evaluated.

Methods: Using the five phases of the framework (theoretical, identification of components of the intervention, definition of trial and intervention design, methodological issues for main trial, promoting effective implementation), corresponding stages in the development of the cluster randomized trial using diagnostic and treatment algorithms to optimize the use of antibiotics in nursing homes are identified and described.

Results: Synthesis of evidence needed to construct the algorithms, survey and qualitative research used to define components of the algorithms, a pilot study to assess the feasibility of delivering the algorithms, methodological issues in the main trial including choice of design, allocation concealment, outcomes, sample size calculation, and analysis are adequately represented using the stages of the framework.

Conclusions: The framework is a useful resource for researchers planning a randomized clinical trial of a complex intervention.

Show MeSH
Related in: MedlinePlus